Initial ResidualBench artifact upload

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 The ResidualBench Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,77 @@
+---
+license: mit
+language:
+- en
+pretty_name: ResidualBench
+size_categories:
+- 1K<n<10K
+task_categories:
+- time-series-forecasting
+tags:
+- benchmark
+- forecasting
+- residual-analysis
+- failure-modes
+- sparse-autoencoder
+- evaluation
+- neurips-2026-eandd
+---
+
+# ResidualBench
+
+A benchmark and evaluation protocol for cross-model forecast failure mode discovery, accompanying the NeurIPS 2026 Evaluations & Datasets Track submission **"ResidualBench: A Benchmark and Evaluation Protocol for Cross-Model Forecast Failure Mode Discovery"**.
+
+This repository hosts the small artifacts required for review and reproduction:
+
+- `croissant.json` — Croissant 1.0 dataset metadata with the full RAI extension.
+- `residualbench-0.1.0.tar.gz` — pip-installable Python package implementing the `fit / encode / reconstruct` method protocol, motif-level metrics, Hungarian alignment, and the lag-1 / learned selectors.
+- `results/*.json` — pre-computed aggregated benchmark results across all 855 (dataset, forecaster, method, seed) cells.
+- `LICENSE`, `REPRODUCE.md` — license and step-by-step reproduction guide.
+
+The full per-window residual tensors (`residuals.pt`, ~123 GB) are not stored in this repository because of size. They can be regenerated from public source datasets in a few GPU-hours by following `REPRODUCE.md` Step 2 ("Train forecasters and collect residuals"). Optionally, a tar archive of the residuals can be uploaded as an additional release file via the `upload_residuals_to_hf.py` helper that ships with the source repository.
+
+## Provenance
+
+ResidualBench builds on nine public time-series datasets (cited in `croissant.json` under `prov:wasDerivedFrom`):
+
+| Dataset | Source | License |
+|---|---|---|
+| ETTh1, ETTh2, ETTm1, ETTm2 | https://github.com/zhouhaoyi/ETDataset | CC-BY-4.0 |
+| Weather | https://www.bgc-jena.mpg.de/wetter/ | CC-BY-4.0 |
+| Electricity | UCI ML Repository (321) | CC-BY-4.0 |
+| Traffic (PEMS) | https://pems.dot.ca.gov/ | Public domain |
+| Exchange | https://github.com/laiguokun/multivariate-time-series-data | CC-BY-4.0 |
+| ILI | CDC FluView | Public domain |
+
+ResidualBench itself does not introduce a new raw dataset.
+
+## Headline numbers (re-derivable from `results/all_results.json`)
+
+- 855 of 945 configurations (Spectral omitted on 6 high-dim datasets).
+- Cross-forecaster Hungarian alignment 0.23–0.57, $4{-}11\times$ above shuffled null on most datasets.
+- Lag-1 model selector beats best fixed forecaster by up to 17 % on temporal hold-out.
+- Cohesion (TopK, mean of 5 forecasters $\times$ 3 seeds): ETTh1 0.481, ETTh2 0.712, ETTm1 0.656, ETTm2 0.615, Weather 0.652, Electricity 0.558, Traffic 0.304, Exchange 0.899, ILI 0.962.
+
+## Reproducing
+
+```bash
+pip install residualbench-0.1.0.tar.gz
+python -c "from residualbench import ResidualBench; print(ResidualBench.__doc__)"
+```
+
+For end-to-end regeneration of the residual tensors and benchmark numbers, see `REPRODUCE.md`.
+
+## Citation
+
+```bibtex
+@inproceedings{residualbench2026,
+  title={ResidualBench: A Benchmark and Evaluation Protocol for Cross-Model Forecast Failure Mode Discovery},
+  author={Anonymous},
+  booktitle={NeurIPS 2026 Evaluations and Datasets Track},
+  year={2026}
+}
+```
+
+## License
+
+MIT for the harness and metadata. Each upstream dataset retains its original license; see `croissant.json` for details.

REPRODUCE.md

@@ -0,0 +1,143 @@
+# Reproducing ResidualBench Results
+
+This guide reproduces the four headline findings reported in the paper
+"ResidualBench: A Benchmark and Evaluation Protocol for Cross-Model Forecast
+Failure Mode Discovery" (NeurIPS 2026 Evaluations & Datasets Track).
+
+## Hardware
+
+- 1 GPU with >=12 GB VRAM (we used NVIDIA A100/3090; CPU works but is slow on
+  N-BEATS / TimesNet / TopK-SAE).
+- ~50 GB free disk for residual artifacts at `H=96`.
+
+## Environment
+
+```bash
+python -m venv .venv && source .venv/bin/activate
+pip install -e ".[dev]"
+```
+
+This installs the `residualbench` package (`pyproject.toml`) and dev
+dependencies (`pytest`, `ruff`, `mypy`).
+
+Sanity check:
+
+```bash
+pytest tests/test_residualbench.py -q
+```
+
+## Step 1 — Datasets (~5 min)
+
+```bash
+python scripts/download_data.py --include-large
+```
+
+Downloads ETTh1/h2, ETTm1/m2, Weather, Electricity, Traffic, Exchange, ILI from
+their public hosts to `data/`. Total ~3 GB.
+
+## Step 2 — Train forecasters and collect residuals (~6-10 GPU-hours)
+
+Trains all 5 forecasters (DLinear, PatchTST, iTransformer, N-BEATS, TimesNet)
+with seed 42 on all 9 datasets at `H=96`, dumping residuals to
+`results/benchmark/<dataset>/<forecaster>/residuals.pt`.
+
+```bash
+python scripts/regimes/run_full_benchmark.py --phase forecasters --device cuda
+```
+
+For the seed-robustness study (Appendix K, 90 additional residuals):
+
+```bash
+python scripts/regimes/run_multi_seed_forecasters.py --gpu 0
+python scripts/regimes/run_multi_seed_forecasters.py --gpu 1   # if available
+```
+
+## Step 3 — Run the decomposition harness (~30 min CPU + ~30 min GPU)
+
+```bash
+python scripts/regimes/run_full_benchmark.py --phase methods --device cuda
+```
+
+Runs the 7 decomposition methods (PCA, Dense AE-16, Dense AE-64, k-means,
+TopK SAE, ICA, Spectral) under the proper train/test protocol on each
+(dataset, forecaster) pair, totalling 855 of 945 configurations (Spectral is
+omitted on 6 high-dim datasets; see paper Section 3.4).
+
+## Step 4 — Reproduce headline findings
+
+### Finding 1 (no single method dominates) and Finding 3 (proper protocol)
+
+```bash
+python scripts/regimes/evaluate_regime_metrics.py
+python scripts/regimes/generate_figures.py        # paper Tables 3, 4 + Figs 2, 3
+```
+
+### Finding 2 (cross-forecaster sharing)
+
+```bash
+python scripts/regimes/evaluate_regime_stability.py --alignment hungarian
+python scripts/regimes/analyze_trivial_similarity.py            # Appendix F
+python scripts/regimes/compute_cross_seed_all.py                # Appendix K
+python scripts/regimes/plot_alignment_sources.py                # Figure 4
+```
+
+### Finding 4 (lag-1 selector + learned selector)
+
+```bash
+python scripts/regimes/evaluate_selectors.py --base-dir results/benchmark
+```
+
+### Statistical tables (Appendix G, J)
+
+```bash
+python scripts/regimes/compute_bootstrap_wilcoxon.py
+```
+
+### Seed-robustness study (Appendix K)
+
+```bash
+python scripts/regimes/compute_forecaster_seed_robustness.py
+```
+
+### Multi-horizon stability (Appendix I)
+
+```bash
+python scripts/regimes/run_multi_horizon.py --datasets ETTh1 Weather \
+    --horizons 48 96 192 336
+```
+
+## Expected outputs
+
+After Step 4 you should have:
+
+- `results/benchmark/analysis.json` — main numbers cited in Sections 4.1-4.2
+- `results/benchmark/selector_comparison.json` — selector / lag-1 numbers
+- `results/benchmark/bootstrap_ci.json`, `wilcoxon_full.json` — Appendix J/G
+- `results/benchmark/forecaster_seed_robustness.json` — Appendix K
+- `paper/figures/*.pdf` — all paper figures regenerated
+
+## End-to-end smoke test (~20 min on a single GPU)
+
+For reviewers who want to verify the pipeline end-to-end on a single small
+dataset:
+
+```bash
+python scripts/regimes/run_full_benchmark.py \
+    --datasets ETTh1 --forecasters dlinear patchtst --device cuda
+python scripts/regimes/evaluate_regime_metrics.py --datasets ETTh1
+```
+
+This runs only ETTh1 with DLinear and PatchTST (the two cheapest forecasters)
+and produces a partial `analysis.json` that should match the ETTh1 row of
+paper Table 3 within seed noise.
+
+## Troubleshooting
+
+- **CUDA OOM on Electricity/Traffic.** Reduce N-BEATS/TimesNet model sizes via
+  `--model-scale 0.5` (paper Section 3.1 documents this).
+- **Spectral clustering hangs on high-dim datasets.** Expected; we omit
+  Spectral on Electricity/Traffic/ILI/Exchange/Weather/ETTm1 and report 855
+  rather than 945 configurations.
+- **ILI Hungarian alignment is heterogeneous.** Expected stress case (3 test
+  windows at `H=96`); paper Section 4.2 reports 4-11x null on the other 8
+  datasets and discusses the ILI exception.

croissant.json

@@ -0,0 +1,182 @@
+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "data": {
+      "@id": "cr:data",
+      "@type": "@json"
+    },
+    "dataType": {
+      "@id": "cr:dataType",
+      "@type": "@vocab"
+    },
+    "dct": "http://purl.org/dc/terms/",
+    "equivalentProperty": "cr:equivalentProperty",
+    "examples": {
+      "@id": "cr:examples",
+      "@type": "@json"
+    },
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "samplingRate": "cr:samplingRate",
+    "replace": "cr:replace",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "name": "ResidualBench",
+  "description": "ResidualBench is a benchmark for systematically comparing methods that discover forecast failure modes from residual windows. It includes pre-computed residuals from five forecasters (DLinear, PatchTST, iTransformer, N-BEATS, TimesNet) across nine named datasets (ETTh1, ETTh2, ETTm1, ETTm2, Weather, Electricity, Traffic, Exchange, ILI) and a Python harness implementing seven decomposition methods (PCA, Dense AE-16, Dense AE-64, k-means, TopK SAE, ICA, Spectral) under a proper train/test evaluation protocol with motif-level metrics validated against synthetic ground truth.",
+  "url": "https://huggingface.co/datasets/EvalData/residualbench",
+  "version": "0.1.0",
+  "license": "https://opensource.org/licenses/MIT",
+  "citeAs": "@inproceedings{residualbench2026, title={ResidualBench: A Benchmark and Evaluation Protocol for Cross-Model Forecast Failure Mode Discovery}, author={Anonymous}, booktitle={NeurIPS 2026 Evaluations and Datasets Track}, year={2026}}",
+  "datePublished": "2026-05-06",
+  "creator": [{"@type": "sc:Person", "name": "Anonymous Author(s)"}],
+
+  "rai:dataCollection": "Pre-computed forecast residuals (r = y - y_hat) were generated by training five forecasters (DLinear, PatchTST, iTransformer, N-BEATS, TimesNet) with input window 336 and forecast horizon 96 on the temporal train splits of nine canonical long-horizon forecasting datasets, then dumping (input, target, prediction, residual) tuples on the held-out validation and test windows. Three independent training seeds (42, 11, 22) are released for every (dataset, forecaster) pair, supporting the seed-robustness analyses in Appendix K of the paper.",
+  "rai:dataPreprocessingProtocol": "Per-channel standardization computed on the training split only, applied identically to validation and test. Temporal 6:2:2 split for ETT datasets, 7:1:2 for Weather, Electricity, Traffic, Exchange and ILI (matching prior long-horizon work). No shuffling. AdamW optimizer, one-cycle learning-rate schedule, gradient clipping at norm 1.0, mixed-precision training, early stopping on validation MSE with patience 10. N-BEATS and TimesNet hidden sizes are reduced on Electricity (321 channels) and Traffic (862 channels) to fit a 24 GB GPU; all other forecaster hyperparameters are held constant across datasets.",
+  "rai:dataAnnotationProtocol": "No human annotation. Pre-computed residuals are deterministic functions of the publicly available source datasets and the trained forecasters. The benchmark also exposes per-window motif-assignment labels produced by each decomposition method; these labels are computed automatically by the released code and are not human-curated.",
+  "rai:dataUseCases": "(i) Evaluating new residual decomposition methods (sparse coding, autoencoders, clustering, regime-switching) under a shared cross-forecaster harness with proper train/test discipline. (ii) Studying cross-architecture failure mode similarity for time-series forecasting research. (iii) Designing portfolio / model-selection strategies that switch between forecasters based on recent error feedback. (iv) Diagnostic analysis of deployed forecasters for failure modes that aggregate metrics conceal.",
+  "rai:dataLimitations": "The four ETT datasets share the power-transformer domain, so cross-domain generalization claims are weakest there; Appendix L of the paper reproduces the four headline findings on the five non-ETT datasets. The ILI dataset has only three held-out windows at horizon 96 and is treated as a small-sample stress case throughout the paper. Spectral clustering is omitted on six high-dimensional datasets where it exceeded a 30-minute compute budget, yielding 855 of a possible 945 (dataset, forecaster, method, seed) configurations. Forecasters are trained at a single horizon (96) for the main results; cross-horizon stability on H in {48, 96, 192, 336} is reported on ETTh1 and Weather only (Appendix I).",
+  "rai:dataBiases": "All nine source datasets are widely used long-horizon forecasting benchmarks and inherit any selection biases of the original collection processes (sensor placement for ETT, station coverage for Weather, customer subset for Electricity, fixed loop detectors for Traffic, country basket for Exchange, US-only for ILI). Cross-forecaster failure-mode sharing is therefore measured on these specific datasets and may not extend to deployments outside the energy / weather / electricity / traffic / finance / medical mix studied here.",
+  "rai:personalSensitiveInformation": "ResidualBench contains no personally identifying information. None of the source datasets release individual-level personal records: ETT records industrial transformer measurements; Weather and Electricity report aggregated sensor or substation-level series; Traffic reports highway loop counts; Exchange reports country-level FX rates; ILI reports CDC-level weekly counts of influenza-like illness with no patient identifiers.",
+  "rai:socialImpact": "ResidualBench is a diagnostic and benchmarking artifact for time-series research and is not intended for direct deployment. The released lag-1 model selector and learned selectors are illustrations of how failure modes can drive portfolio strategies; we explicitly recommend domain-specific validation and human oversight before any safety- or finance-critical deployment, and the paper's broader-impact statement makes this constraint explicit.",
+  "rai:dataSocialImpact": "We anticipate that systematic failure-mode benchmarks lower the cost of detecting silent failures in production forecasters (energy load, traffic, electricity demand) and enable better targeted retraining; the same machinery could in principle be used to optimize forecasters for adversarial financial trading, which we do not condone.",
+  "rai:isSyntheticData": false,
+  "rai:hasSyntheticData": "Optional synthetic motif templates (5 canonical residual patterns with controlled noise) are bundled for metric validation; they are clearly marked, are not used for any of the four headline findings, and exist only to ground the cohesion / VR metrics against ground truth. Synthetic windows live under the path 'synthetic/' inside the artifact and are never mixed with real-world residuals.",
+
+  "prov:wasDerivedFrom": [
+    {"@type": "sc:Dataset", "name": "ETT (Electricity Transformer Temperature)", "url": "https://github.com/zhouhaoyi/ETDataset", "license": "CC-BY-4.0"},
+    {"@type": "sc:Dataset", "name": "Weather", "url": "https://www.bgc-jena.mpg.de/wetter/", "license": "CC-BY-4.0"},
+    {"@type": "sc:Dataset", "name": "Electricity", "url": "https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014", "license": "CC-BY-4.0"},
+    {"@type": "sc:Dataset", "name": "Traffic (PEMS)", "url": "https://pems.dot.ca.gov/", "license": "Public domain (US government)"},
+    {"@type": "sc:Dataset", "name": "Exchange Rate", "url": "https://github.com/laiguokun/multivariate-time-series-data", "license": "CC-BY-4.0"},
+    {"@type": "sc:Dataset", "name": "ILI (Influenza-like Illness)", "url": "https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html", "license": "Public domain (US government)"}
+  ],
+  "prov:wasGeneratedBy": {
+    "@type": "prov:Activity",
+    "rdfs:label": "ResidualBench residual generation pipeline",
+    "description": "Forecasters trained with the released configs in configs/ via 'python scripts/regimes/run_full_benchmark.py --phase forecasters', then residuals dumped via the same script's collection phase. Multi-seed extension produced via scripts/regimes/run_multi_seed_forecasters.py."
+  },
+
+  "distribution": [
+    {
+      "@type": "cr:FileObject",
+      "@id": "residualbench-package",
+      "name": "residualbench-package",
+      "description": "Pip-installable Python package implementing the fit/encode/reconstruct method protocol, motif-level metrics (cohesion, VR), Hungarian alignment, lag-1 / learned selectors, and the proper train/test harness.",
+      "contentUrl": "https://huggingface.co/datasets/EvalData/residualbench/resolve/main/residualbench-0.1.0.tar.gz",
+      "encodingFormat": "application/gzip",
+      "md5": "PLACEHOLDER_MD5_TO_BE_FILLED_AT_RELEASE"
+    },
+    {
+      "@type": "cr:FileSet",
+      "@id": "residual-tensors",
+      "name": "residual-tensors",
+      "description": "Pre-computed forecaster residuals as PyTorch tensors. One file per (dataset, forecaster, seed) tuple, totalling 9 datasets x 5 forecasters x 3 seeds = 135 files. Each file contains keys 'train_input', 'train_target', 'train_pred', 'train_residual' and the corresponding 'test_*' splits.",
+      "containedIn": {"@id": "residualbench-package"},
+      "encodingFormat": "application/x-pytorch",
+      "includes": "results/benchmark*/{ETTh1,ETTh2,ETTm1,ETTm2,Weather,Electricity,Traffic,Exchange,ILI}/{dlinear,patchtst,itransformer,nbeats,timesnet}/seed_{42,11,22}/residuals.pt"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "results-summary",
+      "name": "results-summary",
+      "description": "Aggregated benchmark results JSON: one row per (dataset, forecaster, method, seed) cell with cohesion, VR, and reconstruction MSE for the 855 reported configurations.",
+      "contentUrl": "https://huggingface.co/datasets/EvalData/residualbench/resolve/main/all_results.json",
+      "encodingFormat": "application/json",
+      "md5": "PLACEHOLDER_MD5_TO_BE_FILLED_AT_RELEASE"
+    }
+  ],
+
+  "recordSet": [
+    {
+      "@type": "cr:RecordSet",
+      "@id": "benchmark-cells",
+      "name": "benchmark-cells",
+      "description": "One record per (dataset, forecaster, method, seed) benchmark cell.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "benchmark-cells/dataset",
+          "name": "dataset",
+          "description": "Source dataset identifier (one of ETTh1, ETTh2, ETTm1, ETTm2, Weather, Electricity, Traffic, Exchange, ILI).",
+          "dataType": "sc:Text",
+          "source": {"fileObject": {"@id": "results-summary"}, "extract": {"jsonPath": "$[*].dataset"}}
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "benchmark-cells/forecaster",
+          "name": "forecaster",
+          "description": "Base forecaster identifier (dlinear, patchtst, itransformer, nbeats, timesnet).",
+          "dataType": "sc:Text",
+          "source": {"fileObject": {"@id": "results-summary"}, "extract": {"jsonPath": "$[*].forecaster"}}
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "benchmark-cells/method",
+          "name": "method",
+          "description": "Decomposition method identifier (PCA, Dense_AE_16, Dense_AE_64, k-means, TopK, ICA, Spectral).",
+          "dataType": "sc:Text",
+          "source": {"fileObject": {"@id": "results-summary"}, "extract": {"jsonPath": "$[*].method"}}
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "benchmark-cells/seed",
+          "name": "seed",
+          "description": "Random seed for the decomposition fit (42, 11, or 22).",
+          "dataType": "sc:Integer",
+          "source": {"fileObject": {"@id": "results-summary"}, "extract": {"jsonPath": "$[*].seed"}}
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "benchmark-cells/cohesion",
+          "name": "cohesion",
+          "description": "Motif cohesion metric (paper Section 3.2). Higher is better; bounded in [0, 1].",
+          "dataType": "sc:Float",
+          "source": {"fileObject": {"@id": "results-summary"}, "extract": {"jsonPath": "$[*].cohesion"}}
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "benchmark-cells/vr",
+          "name": "vr",
+          "description": "Variance ratio (within-motif vs across-motif residual variance). Higher (more positive) is better.",
+          "dataType": "sc:Float",
+          "source": {"fileObject": {"@id": "results-summary"}, "extract": {"jsonPath": "$[*].vr"}}
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "benchmark-cells/recon_mse",
+          "name": "recon_mse",
+          "description": "Reconstruction mean squared error of the decomposition method on test residuals.",
+          "dataType": "sc:Float",
+          "source": {"fileObject": {"@id": "results-summary"}, "extract": {"jsonPath": "$[*].recon_mse"}}
+        }
+      ]
+    }
+  ]
+}

residualbench-0.1.0.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aee235c8dd33b26d726e7501dbf1e50f15672d1d4741bb4381ae43bf638702a0
+size 9319

results/analysis.json

@@ -0,0 +1,1198 @@
+{
+  "cross_forecaster_alignment": {
+    "ETTh1": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.3917243182659149
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.4144817292690277
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.4158850312232971
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.34682315587997437
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.44512152671813965
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.32198566198349
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.35455185174942017
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.33124181628227234
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.5160236358642578
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.4339941143989563
+        }
+      ],
+      "avg_hungarian": 0.39718328416347504
+    },
+    "ETTh2": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.6566316485404968
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.6085385680198669
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.670150876045227
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.5255814790725708
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.5073775053024292
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.5784513354301453
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.4805181920528412
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.613020122051239
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.5082992911338806
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.5459529161453247
+        }
+      ],
+      "avg_hungarian": 0.5694521933794021
+    },
+    "ETTm1": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.3361435830593109
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.4082643389701843
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.3879271149635315
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.34676963090896606
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.3493657112121582
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.41647329926490784
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.48560285568237305
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.5233218669891357
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.33303552865982056
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.4601266384124756
+        }
+      ],
+      "avg_hungarian": 0.40470305681228635
+    },
+    "ETTm2": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.5674955248832703
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.4423693120479584
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.46702688932418823
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.4925690293312073
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.46630486845970154
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.43643292784690857
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.5034961700439453
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.6410799622535706
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.3917478621006012
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.41232556104660034
+        }
+      ],
+      "avg_hungarian": 0.48208481073379517
+    },
+    "Weather": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.5331443548202515
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.5373182892799377
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.44465479254722595
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.4408039152622223
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.49384865164756775
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.5199095606803894
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.47577935457229614
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.39638644456863403
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.47289949655532837
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.4815041422843933
+        }
+      ],
+      "avg_hungarian": 0.47962490022182463
+    },
+    "Electricity": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.6449966430664062
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.44181710481643677
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.329089879989624
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.45924100279808044
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.4865364134311676
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.36666539311408997
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.4859023988246918
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.4933294355869293
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.3896159529685974
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.349383682012558
+        }
+      ],
+      "avg_hungarian": 0.44465779066085814
+    },
+    "Traffic": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.4476960301399231
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.49592089653015137
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.42686742544174194
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.2309916466474533
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.5162879824638367
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.666651725769043
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.322756826877594
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.47335949540138245
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.2564678490161896
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.27263781428337097
+        }
+      ],
+      "avg_hungarian": 0.4109637692570686
+    },
+    "Exchange": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.5081571340560913
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.44859081506729126
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": 0.4897000193595886
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": 0.38180118799209595
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.6817030906677246
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": 0.7716380953788757
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.6080588102340698
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.7027053833007812
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.5219091176986694
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.5373969078063965
+        }
+      ],
+      "avg_hungarian": 0.5651660561561584
+    },
+    "ILI": {
+      "pairwise": [
+        {
+          "fc_a": "dlinear",
+          "fc_b": "patchtst",
+          "hungarian": 0.4560392498970032
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "itransformer",
+          "hungarian": 0.39727115631103516
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "nbeats",
+          "hungarian": -0.012977194972336292
+        },
+        {
+          "fc_a": "dlinear",
+          "fc_b": "timesnet",
+          "hungarian": -0.013514824211597443
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "itransformer",
+          "hungarian": 0.41870737075805664
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "nbeats",
+          "hungarian": -0.004956719931215048
+        },
+        {
+          "fc_a": "patchtst",
+          "fc_b": "timesnet",
+          "hungarian": 0.02250385470688343
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "nbeats",
+          "hungarian": 0.05019726604223251
+        },
+        {
+          "fc_a": "itransformer",
+          "fc_b": "timesnet",
+          "hungarian": 0.11535587161779404
+        },
+        {
+          "fc_a": "nbeats",
+          "fc_b": "timesnet",
+          "hungarian": 0.868864893913269
+        }
+      ],
+      "avg_hungarian": 0.22974909241311253
+    }
+  },
+  "raw_residual_similarity": {
+    "ETTh1": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.9413118958473206
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.9389805793762207
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.9102738499641418
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.8290320038795471
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.9623098969459534
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.928828239440918
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.8440625667572021
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.9210187792778015
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.831081211566925
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.8111209869384766
+        }
+      ],
+      "avg_cosine": 0.8918020009994507
+    },
+    "ETTh2": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.9073172807693481
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.8843784928321838
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.8951956033706665
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.671205461025238
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.924527108669281
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.9370428323745728
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.678984522819519
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.8984293937683105
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.6689125895500183
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.6450047492980957
+        }
+      ],
+      "avg_cosine": 0.8110998034477234
+    },
+    "ETTm1": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.9024958610534668
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.8710768222808838
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.8725137114524841
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.7942776679992676
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.8869780898094177
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.8957141637802124
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.7811936140060425
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.8963271379470825
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.7862645983695984
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.7860975861549377
+        }
+      ],
+      "avg_cosine": 0.8472939252853393
+    },
+    "ETTm2": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.9283076524734497
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.9115669131278992
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.9159248471260071
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.8205886483192444
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.938172459602356
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.9309675097465515
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.8227680921554565
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.9274762272834778
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.8088763952255249
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.8082635998725891
+        }
+      ],
+      "avg_cosine": 0.8812912344932556
+    },
+    "Weather": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.8870323300361633
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.8484342694282532
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.8792977333068848
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.7354639172554016
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.8689866065979004
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.901665449142456
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.7185164093971252
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.8730499148368835
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.6989345550537109
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.6739211678504944
+        }
+      ],
+      "avg_cosine": 0.8085302352905274
+    },
+    "Electricity": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.8804688453674316
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.878393828868866
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.873329222202301
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.5567358732223511
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.882013201713562
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.8704409003257751
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.5418177247047424
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.921269416809082
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.53231281042099
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.5250846743583679
+        }
+      ],
+      "avg_cosine": 0.7461866497993469
+    },
+    "Traffic": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.7863703966140747
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.8576543927192688
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.7953134179115295
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.6933470368385315
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.9060727953910828
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.9263131022453308
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.8380256295204163
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.9356461763381958
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.7966657280921936
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.8214770555496216
+        }
+      ],
+      "avg_cosine": 0.8356885731220245
+    },
+    "Exchange": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.6286680102348328
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.7330744862556458
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": 0.7477949857711792
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": 0.30153316259384155
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.7123157382011414
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": 0.8979324698448181
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": 0.6002172827720642
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": 0.7519702911376953
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": 0.3576586842536926
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.5443481206893921
+        }
+      ],
+      "avg_cosine": 0.6275513231754303
+    },
+    "ILI": {
+      "pairwise": [
+        {
+          "a": "dlinear",
+          "b": "patchtst",
+          "mean_cosine": 0.8324419856071472
+        },
+        {
+          "a": "dlinear",
+          "b": "itransformer",
+          "mean_cosine": 0.8868004679679871
+        },
+        {
+          "a": "dlinear",
+          "b": "nbeats",
+          "mean_cosine": -0.06752055138349533
+        },
+        {
+          "a": "dlinear",
+          "b": "timesnet",
+          "mean_cosine": -0.04067131504416466
+        },
+        {
+          "a": "patchtst",
+          "b": "itransformer",
+          "mean_cosine": 0.9425480365753174
+        },
+        {
+          "a": "patchtst",
+          "b": "nbeats",
+          "mean_cosine": -0.07654145359992981
+        },
+        {
+          "a": "patchtst",
+          "b": "timesnet",
+          "mean_cosine": -0.08827263116836548
+        },
+        {
+          "a": "itransformer",
+          "b": "nbeats",
+          "mean_cosine": -0.06488572806119919
+        },
+        {
+          "a": "itransformer",
+          "b": "timesnet",
+          "mean_cosine": -0.039749376475811005
+        },
+        {
+          "a": "nbeats",
+          "b": "timesnet",
+          "mean_cosine": 0.8701958060264587
+        }
+      ],
+      "avg_cosine": 0.3154345240443945
+    }
+  },
+  "model_selection": {
+    "ETTh1": {
+      "individual": {
+        "dlinear": 0.4284210503101349,
+        "patchtst": 0.42498481273651123,
+        "itransformer": 0.44583621621131897,
+        "nbeats": 0.43018683791160583,
+        "timesnet": 0.5317039489746094
+      },
+      "best_fixed": 0.42498481273651123,
+      "best_forecaster": "patchtst",
+      "ensemble": 0.41321390867233276,
+      "lag1": 0.3828434646129608,
+      "oracle": 0.3786303699016571,
+      "lag1_vs_best_fixed_pct": 9.915965667619721,
+      "lag1_vs_ensemble_pct": 7.3498116646056255,
+      "n_forecasters": 5,
+      "n_windows": 3293
+    },
+    "ETTh2": {
+      "individual": {
+        "dlinear": 0.17053967714309692,
+        "patchtst": 0.1721627563238144,
+        "itransformer": 0.18731674551963806,
+        "nbeats": 0.1779528260231018,
+        "timesnet": 0.3002372980117798
+      },
+      "best_fixed": 0.17053967714309692,
+      "best_forecaster": "dlinear",
+      "ensemble": 0.16783994436264038,
+      "lag1": 0.1538228839635849,
+      "oracle": 0.15236607193946838,
+      "lag1_vs_best_fixed_pct": 9.802289683875296,
+      "lag1_vs_ensemble_pct": 8.351444855564162,
+      "n_forecasters": 5,
+      "n_windows": 3293
+    },
+    "ETTm1": {
+      "individual": {
+        "dlinear": 0.3680061101913452,
+        "patchtst": 0.37795835733413696,
+        "itransformer": 0.3930971026420593,
+        "nbeats": 0.3944958746433258,
+        "timesnet": 0.502124011516571
+      },
+      "best_fixed": 0.3680061101913452,
+      "best_forecaster": "dlinear",
+      "ensemble": 0.3598978817462921,
+      "lag1": 0.3141799569129944,
+      "oracle": 0.31101447343826294,
+      "lag1_vs_best_fixed_pct": 14.626429232483085,
+      "lag1_vs_ensemble_pct": 12.703026928490319,
+      "n_forecasters": 5,
+      "n_windows": 13745
+    },
+    "ETTm2": {
+      "individual": {
+        "dlinear": 0.11874929815530777,
+        "patchtst": 0.11810851842164993,
+        "itransformer": 0.11961802840232849,
+        "nbeats": 0.12195854634046555,
+        "timesnet": 0.12634912133216858
+      },
+      "best_fixed": 0.11810851842164993,
+      "best_forecaster": "patchtst",
+      "ensemble": 0.11120489239692688,
+      "lag1": 0.10096410661935806,
+      "oracle": 0.099876768887043,
+      "lag1_vs_best_fixed_pct": 14.515813111028923,
+      "lag1_vs_ensemble_pct": 9.208934568288669,
+      "n_forecasters": 5,
+      "n_windows": 13745
+    },
+    "Weather": {
+      "individual": {
+        "dlinear": 0.15998028218746185,
+        "patchtst": 0.1749560832977295,
+        "itransformer": 0.1708894670009613,
+        "nbeats": 0.16512899100780487,
+        "timesnet": 0.1986662745475769
+      },
+      "best_fixed": 0.15998028218746185,
+      "best_forecaster": "dlinear",
+      "ensemble": 0.15199188888072968,
+      "lag1": 0.14080192148685455,
+      "oracle": 0.13999192416667938,
+      "lag1_vs_best_fixed_pct": 11.987952789165893,
+      "lag1_vs_ensemble_pct": 7.362213520917591,
+      "n_forecasters": 5,
+      "n_windows": 10349
+    },
+    "Electricity": {
+      "individual": {
+        "dlinear": 0.185342937707901,
+        "patchtst": 0.2076812982559204,
+        "itransformer": 0.16043002903461456,
+        "nbeats": 0.16774685680866241,
+        "timesnet": 0.3241107761859894
+      },
+      "best_fixed": 0.16043002903461456,
+      "best_forecaster": "itransformer",
+      "ensemble": 0.1614314615726471,
+      "lag1": 0.15918664634227753,
+      "oracle": 0.1588297188282013,
+      "lag1_vs_best_fixed_pct": 0.7750311458640716,
+      "lag1_vs_ensemble_pct": 1.3905686094276983,
+      "n_forecasters": 5,
+      "n_windows": 5071
+    },
+    "Traffic": {
+      "individual": {
+        "dlinear": 0.6506800055503845,
+        "patchtst": 0.6239721179008484,
+        "itransformer": 0.4875304102897644,
+        "nbeats": 0.505207359790802,
+        "timesnet": 0.6958082914352417
+      },
+      "best_fixed": 0.4875304102897644,
+      "best_forecaster": "itransformer",
+      "ensemble": 0.5124132633209229,
+      "lag1": 0.48077335953712463,
+      "oracle": 0.4791821241378784,
+      "lag1_vs_best_fixed_pct": 1.3859752356009356,
+      "lag1_vs_ensemble_pct": 6.1746847805503124,
+      "n_forecasters": 5,
+      "n_windows": 3319
+    },
+    "Exchange": {
+      "individual": {
+        "dlinear": 0.08734697103500366,
+        "patchtst": 0.18853969871997833,
+        "itransformer": 0.104995496571064,
+        "nbeats": 0.10820479691028595,
+        "timesnet": 0.8384765386581421
+      },
+      "best_fixed": 0.08734697103500366,
+      "best_forecaster": "dlinear",
+      "ensemble": 0.1434914469718933,
+      "lag1": 0.07545731961727142,
+      "oracle": 0.07524363696575165,
+      "lag1_vs_best_fixed_pct": 13.611979072482717,
+      "lag1_vs_ensemble_pct": 47.413367688701484,
+      "n_forecasters": 5,
+      "n_windows": 1328
+    },
+    "ILI": {
+      "individual": {
+        "dlinear": 0.1644892543554306,
+        "patchtst": 0.19756509363651276,
+        "itransformer": 0.18365304172039032,
+        "nbeats": 4.9091105461120605,
+        "timesnet": 8.407790184020996
+      },
+      "best_fixed": 0.1644892543554306,
+      "best_forecaster": "dlinear",
+      "ensemble": 0.5798237323760986,
+      "lag1": 0.16306203603744507,
+      "oracle": 0.1644892543554306,
+      "lag1_vs_best_fixed_pct": 0.8676665983916384,
+      "lag1_vs_ensemble_pct": 71.8773090971592,
+      "n_forecasters": 5,
+      "n_windows": 3
+    }
+  },
+  "temporal_persistence": {
+    "ETTh1": {
+      "same_consecutive": 0.7475698663426489,
+      "autocorrelation": 0.7208871466577207
+    },
+    "ETTh2": {
+      "same_consecutive": 0.8177399756986634,
+      "autocorrelation": 0.788890380493872
+    },
+    "ETTm1": {
+      "same_consecutive": 0.8397846332945286,
+      "autocorrelation": 0.8247269742760446
+    },
+    "ETTm2": {
+      "same_consecutive": 0.8332363213038417,
+      "autocorrelation": 0.8027599788974071
+    },
+    "Weather": {
+      "same_consecutive": 0.8959219172787012,
+      "autocorrelation": 0.8847372634922889
+    },
+    "Electricity": {
+      "same_consecutive": 0.8272189349112427,
+      "autocorrelation": 0.8157065960442305
+    },
+    "Traffic": {
+      "same_consecutive": 0.5864978902953587,
+      "autocorrelation": 0.5981806238516669
+    },
+    "Exchange": {
+      "same_consecutive": 0.945742275810098,
+      "autocorrelation": 0.9512318955462541
+    },
+    "ILI": {
+      "same_consecutive": 0.7196233292831106,
+      "autocorrelation": 0.7277764531786444
+    }
+  }
+}

results/bootstrap_ci.json

@@ -0,0 +1,419 @@
+{
+  "ETTh1": {
+    "PCA": {
+      "mean": 0.4171050029496352,
+      "std": 0.02017482683193158,
+      "lo": 0.40663983131945136,
+      "hi": 0.4268444821561377,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.44133831739425666,
+      "std": 0.04330318940656087,
+      "lo": 0.42027249319354704,
+      "hi": 0.4642491171571116,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.3633657016605139,
+      "std": 0.04978748505233282,
+      "lo": 0.3393053740188479,
+      "hi": 0.38969111587852234,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.476032989025116,
+      "std": 0.05717880507088909,
+      "lo": 0.44699916677673657,
+      "hi": 0.5046371398766835,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.4811991792917251,
+      "std": 0.04950956126908989,
+      "lo": 0.45457358254988994,
+      "hi": 0.5047171689346432,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.44799140227337675,
+      "std": 0.06701050344223636,
+      "lo": 0.41487602232831217,
+      "hi": 0.48229445148569844,
+      "n": 15
+    },
+    "Spectral": {
+      "mean": 0.49429177751143777,
+      "std": 0.055319879705792115,
+      "lo": 0.46784805352489156,
+      "hi": 0.5240810924892625,
+      "n": 15
+    }
+  },
+  "ETTh2": {
+    "PCA": {
+      "mean": 0.5909992029331623,
+      "std": 0.08248631989131802,
+      "lo": 0.5527946110442282,
+      "hi": 0.6349363289139387,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.7273528537899256,
+      "std": 0.1032626957442987,
+      "lo": 0.6733844137620181,
+      "hi": 0.7785681246394912,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.49972763260205594,
+      "std": 0.10595213101400568,
+      "lo": 0.4474801321122795,
+      "hi": 0.5537660083578279,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.7472399912277858,
+      "std": 0.058154817216853553,
+      "lo": 0.7164496061454215,
+      "hi": 0.7756895274718603,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.7116594993074735,
+      "std": 0.07363183647821564,
+      "lo": 0.6730139159485697,
+      "hi": 0.7482887026493749,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.7010225590815148,
+      "std": 0.05964826423157641,
+      "lo": 0.6714006940616914,
+      "hi": 0.7317077856442581,
+      "n": 15
+    },
+    "Spectral": {
+      "mean": 0.6587891900539398,
+      "std": 0.07571428356851582,
+      "lo": 0.6213616846352814,
+      "hi": 0.6982565017143884,
+      "n": 15
+    }
+  },
+  "ETTm1": {
+    "PCA": {
+      "mean": 0.5414499825487534,
+      "std": 0.06245410393276972,
+      "lo": 0.5120108606846384,
+      "hi": 0.5747508012590309,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.6701205569505692,
+      "std": 0.07334359537430507,
+      "lo": 0.6300013130009174,
+      "hi": 0.7045724909951289,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.5176160377884904,
+      "std": 0.0579687922043034,
+      "lo": 0.4889516353309155,
+      "hi": 0.5478790149291356,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.6482138058543204,
+      "std": 0.06395880390850343,
+      "lo": 0.6169110023627677,
+      "hi": 0.6815693052957456,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.6559190084536869,
+      "std": 0.08574720620155835,
+      "lo": 0.6102849685798089,
+      "hi": 0.6967691410879294,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.6264328748111923,
+      "std": 0.09484046375643616,
+      "lo": 0.5780357275825615,
+      "hi": 0.6736124700295428,
+      "n": 15
+    }
+  },
+  "ETTm2": {
+    "PCA": {
+      "mean": 0.6176143797238667,
+      "std": 0.08785988086382844,
+      "lo": 0.5725718692938488,
+      "hi": 0.6612977904056511,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.730765559002757,
+      "std": 0.08357329077451187,
+      "lo": 0.6887776929456741,
+      "hi": 0.7732386375765005,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.5603966174523035,
+      "std": 0.07291793700222014,
+      "lo": 0.523429225794971,
+      "hi": 0.5975282975807785,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.6921540379524231,
+      "std": 0.04466997126291788,
+      "lo": 0.6700606491565705,
+      "hi": 0.7155470426281293,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.6153921738266945,
+      "std": 0.10294459871841821,
+      "lo": 0.5625819981830815,
+      "hi": 0.6662889900815984,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.6943691419065,
+      "std": 0.08181238391104079,
+      "lo": 0.6520167949000993,
+      "hi": 0.735187525847306,
+      "n": 15
+    }
+  },
+  "Electricity": {
+    "PCA": {
+      "mean": 0.505505721271038,
+      "std": 0.06821991570414188,
+      "lo": 0.4710694490646322,
+      "hi": 0.5390829782485962,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.5306520505746205,
+      "std": 0.0953563636146602,
+      "lo": 0.4828590859323739,
+      "hi": 0.5799027872309088,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.43628965241213635,
+      "std": 0.06663485705717168,
+      "lo": 0.40455474710712824,
+      "hi": 0.47129010761342943,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.4905926613012949,
+      "std": 0.0710098455700965,
+      "lo": 0.4558877802590529,
+      "hi": 0.5281948353225987,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.5581442653139432,
+      "std": 0.07244736448376479,
+      "lo": 0.5220791275476415,
+      "hi": 0.5948737118716041,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.5119286266962688,
+      "std": 0.039220387722521614,
+      "lo": 0.49312339047094184,
+      "hi": 0.5328931267708541,
+      "n": 15
+    }
+  },
+  "Exchange": {
+    "PCA": {
+      "mean": 0.8362344759702681,
+      "std": 0.06945981190697578,
+      "lo": 0.8033959205796319,
+      "hi": 0.8734726117054621,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.8692389285564422,
+      "std": 0.0644536841369415,
+      "lo": 0.8358689662714798,
+      "hi": 0.9008550885866086,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.8435728583733241,
+      "std": 0.09257460722651296,
+      "lo": 0.7970963558057945,
+      "hi": 0.8911276995937027,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.8858832160631815,
+      "std": 0.06777503046465644,
+      "lo": 0.8523267873972654,
+      "hi": 0.9206653466274339,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.8985308420658111,
+      "std": 0.05543222484725031,
+      "lo": 0.8697876564115287,
+      "hi": 0.9266269520620504,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.8435129668315253,
+      "std": 0.07803852294322318,
+      "lo": 0.8036838174511988,
+      "hi": 0.8833534657359123,
+      "n": 15
+    },
+    "Spectral": {
+      "mean": 0.8962228451172509,
+      "std": 0.05207959366147481,
+      "lo": 0.8708257204989592,
+      "hi": 0.9226725072960058,
+      "n": 15
+    }
+  },
+  "ILI": {
+    "PCA": {
+      "mean": 0.9615951073169708,
+      "std": 0.00916162377245985,
+      "lo": 0.9570130561689535,
+      "hi": 0.9662291590372721,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.961595103740692,
+      "std": 0.009161618410834252,
+      "lo": 0.957013054271539,
+      "hi": 0.9662291519443194,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.9615950993696849,
+      "std": 0.009161615404495468,
+      "lo": 0.9570130522052448,
+      "hi": 0.9662291479210059,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.9615950949986776,
+      "std": 0.009161625589055058,
+      "lo": 0.9570130437811216,
+      "hi": 0.9662291495203972,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.9615951037406921,
+      "std": 0.009161616398373026,
+      "lo": 0.9570130550563335,
+      "hi": 0.966229150334994,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.9615951005617777,
+      "std": 0.009161619603889306,
+      "lo": 0.957013051768144,
+      "hi": 0.9662291519045829,
+      "n": 15
+    }
+  },
+  "Traffic": {
+    "PCA": {
+      "mean": 0.19156031923989456,
+      "std": 0.06323082891795057,
+      "lo": 0.16218154005457958,
+      "hi": 0.22527570632534724,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.27530888666709263,
+      "std": 0.03509368582575563,
+      "lo": 0.25884113388881086,
+      "hi": 0.294100108464559,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.25141616985201837,
+      "std": 0.043905879523783935,
+      "lo": 0.2283473643325269,
+      "hi": 0.27329780761400857,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.3265250971913337,
+      "std": 0.058093409904782144,
+      "lo": 0.2981521984164913,
+      "hi": 0.3572576774830619,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.3044405508041381,
+      "std": 0.04120184951938422,
+      "lo": 0.28335448436687394,
+      "hi": 0.3251067703391115,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.21332372726115864,
+      "std": 0.07265552975238612,
+      "lo": 0.17865539660112215,
+      "hi": 0.25137353374827454,
+      "n": 15
+    }
+  },
+  "Weather": {
+    "PCA": {
+      "mean": 0.6775962777932484,
+      "std": 0.13094326921888427,
+      "lo": 0.6087645049790541,
+      "hi": 0.7452214850435652,
+      "n": 15
+    },
+    "Dense_AE_16": {
+      "mean": 0.7185343440373739,
+      "std": 0.03965544040502964,
+      "lo": 0.6986892816871404,
+      "hi": 0.7394776238749424,
+      "n": 15
+    },
+    "Dense_AE_64": {
+      "mean": 0.5670199275016784,
+      "std": 0.03441448387661883,
+      "lo": 0.5494945414947967,
+      "hi": 0.5838231634249289,
+      "n": 15
+    },
+    "k-means": {
+      "mean": 0.6846980163455009,
+      "std": 0.034765850554510154,
+      "lo": 0.6666888307208815,
+      "hi": 0.7021394025683403,
+      "n": 15
+    },
+    "TopK": {
+      "mean": 0.6523081323504447,
+      "std": 0.04879831216527077,
+      "lo": 0.6273359005327025,
+      "hi": 0.6758972798809408,
+      "n": 15
+    },
+    "ICA": {
+      "mean": 0.6845697929337621,
+      "std": 0.057795254437391134,
+      "lo": 0.6547329414213697,
+      "hi": 0.713166535012424,
+      "n": 15
+    }
+  }
+}

results/cross_seed_stability_all.json

@@ -0,0 +1,162 @@
+{
+  "ETTh1": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.47407999634742737
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.40165719389915466
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.4205342233181
+      }
+    ],
+    "avg_hungarian": 0.43209047118822735
+  },
+  "ETTh2": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.7324333190917969
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.6771837472915649
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.7492242455482483
+      }
+    ],
+    "avg_hungarian": 0.71961377064387
+  },
+  "ETTm1": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.32310760021209717
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.37185078859329224
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.33248990774154663
+      }
+    ],
+    "avg_hungarian": 0.3424827655156453
+  },
+  "ETTm2": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.6236665844917297
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.602129340171814
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.6060845255851746
+      }
+    ],
+    "avg_hungarian": 0.6106268167495728
+  },
+  "Weather": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.5564063787460327
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.5065554976463318
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.5258375406265259
+      }
+    ],
+    "avg_hungarian": 0.5295998056729635
+  },
+  "Electricity": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.5426796674728394
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.5972782969474792
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.6800534725189209
+      }
+    ],
+    "avg_hungarian": 0.6066704789797465
+  },
+  "Traffic": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.4429459571838379
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.6760066747665405
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.43701499700546265
+      }
+    ],
+    "avg_hungarian": 0.5186558763186137
+  },
+  "Exchange": {
+    "pairwise": [
+      {
+        "seed_a": 42,
+        "seed_b": 11,
+        "hungarian": 0.6659791469573975
+      },
+      {
+        "seed_a": 42,
+        "seed_b": 22,
+        "hungarian": 0.6395673155784607
+      },
+      {
+        "seed_a": 11,
+        "seed_b": 22,
+        "hungarian": 0.5506600141525269
+      }
+    ],
+    "avg_hungarian": 0.6187354922294617
+  }
+}

results/forecaster_seed_robustness.json

@@ -0,0 +1,1115 @@
+{
+  "ETTh1": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.4285186032454173,
+      "test_mse_std": 8.464919479660812e-05,
+      "test_mse_cv": 0.000197539136353734,
+      "test_mse_per_seed": [
+        0.4284210801124573,
+        0.42862749099731445,
+        0.4285072386264801
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.35714393854141235
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.46130675077438354
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.4426179528236389
+          }
+        ],
+        "avg": 0.42035621404647827
+      }
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.4193827609221141,
+      "test_mse_std": 0.004028302473894778,
+      "test_mse_cv": 0.009605312495529345,
+      "test_mse_per_seed": [
+        0.42498478293418884,
+        0.41568517684936523,
+        0.4174783229827881
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.4074513018131256
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.48595690727233887
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.33207255601882935
+          }
+        ],
+        "avg": 0.40849358836809796
+      }
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.44281793634096783,
+      "test_mse_std": 0.0021343506770908766,
+      "test_mse_cv": 0.004819928241225162,
+      "test_mse_per_seed": [
+        0.4458361864089966,
+        0.44128045439720154,
+        0.4413371682167053
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.49984121322631836
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.4384981095790863
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.45309072732925415
+          }
+        ],
+        "avg": 0.4638100167115529
+      }
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.4248859981695811,
+      "test_mse_std": 0.004301619516543652,
+      "test_mse_cv": 0.010124173390215565,
+      "test_mse_per_seed": [
+        0.43018677830696106,
+        0.42482060194015503,
+        0.4196506142616272
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.3871736526489258
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.5116091370582581
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.4198659062385559
+          }
+        ],
+        "avg": 0.4395495653152466
+      }
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.5414683818817139,
+      "test_mse_std": 0.007496344279962241,
+      "test_mse_cv": 0.013844472790656594,
+      "test_mse_per_seed": [
+        0.5317039489746094,
+        0.5499261617660522,
+        0.54277503490448
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.4020785391330719
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.44921165704727173
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.3021703064441681
+          }
+        ],
+        "avg": 0.3844868342081706
+      }
+    }
+  },
+  "ETTh2": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.1758549908796946,
+      "test_mse_std": 0.0049869009628153655,
+      "test_mse_cv": 0.028358029180002,
+      "test_mse_per_seed": [
+        0.17053967714309692,
+        0.18252691626548767,
+        0.17449837923049927
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.6673052906990051
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.675568163394928
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.7030343413352966
+          }
+        ],
+        "avg": 0.6819692651430765
+      }
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.17122980952262878,
+      "test_mse_std": 0.0012401274403353445,
+      "test_mse_cv": 0.007242473981561348,
+      "test_mse_per_seed": [
+        0.1721627414226532,
+        0.17204946279525757,
+        0.16947722434997559
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5551444888114929
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.545086681842804
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.6120994687080383
+          }
+        ],
+        "avg": 0.5707768797874451
+      }
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.19782911241054535,
+      "test_mse_std": 0.007558027968717399,
+      "test_mse_cv": 0.038204831819861694,
+      "test_mse_per_seed": [
+        0.18731674551963806,
+        0.20141099393367767,
+        0.2047595977783203
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.6611559391021729
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.5881480574607849
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.6753238439559937
+          }
+        ],
+        "avg": 0.6415426135063171
+      }
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.17810101807117462,
+      "test_mse_std": 0.001046018698362593,
+      "test_mse_cv": 0.005873176412414284,
+      "test_mse_per_seed": [
+        0.1779528111219406,
+        0.1769004613161087,
+        0.17944978177547455
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.6809654831886292
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.6398415565490723
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.6901015043258667
+          }
+        ],
+        "avg": 0.6703028480211893
+      }
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.31532930334409076,
+      "test_mse_std": 0.016607955006470422,
+      "test_mse_cv": 0.05266860653400056,
+      "test_mse_per_seed": [
+        0.3002372682094574,
+        0.33846086263656616,
+        0.3072897791862488
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5992431044578552
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.4795438349246979
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5791829824447632
+          }
+        ],
+        "avg": 0.5526566406091055
+      }
+    }
+  },
+  "ETTm1": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.36607589324315387,
+      "test_mse_std": 0.0013725846330284184,
+      "test_mse_cv": 0.003749453756346431,
+      "test_mse_per_seed": [
+        0.36800616979599,
+        0.36528825759887695,
+        0.3649332523345947
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.42171916365623474
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.37742164731025696
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.36056047677993774
+          }
+        ],
+        "avg": 0.3865670959154765
+      }
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.38716553648312885,
+      "test_mse_std": 0.007082503519540524,
+      "test_mse_cv": 0.018293217892985554,
+      "test_mse_per_seed": [
+        0.37795835733413696,
+        0.3883539140224457,
+        0.39518433809280396
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.44971880316734314
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.32122498750686646
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.3801648020744324
+          }
+        ],
+        "avg": 0.3837028642495473
+      }
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.3918092946211497,
+      "test_mse_std": 0.003077357286160554,
+      "test_mse_cv": 0.007854222266820211,
+      "test_mse_per_seed": [
+        0.3930971324443817,
+        0.39476555585861206,
+        0.3875651955604553
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.504799485206604
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.5199573040008545
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5074702501296997
+          }
+        ],
+        "avg": 0.5107423464457194
+      }
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.39731742938359577,
+      "test_mse_std": 0.0027864703998892165,
+      "test_mse_cv": 0.007013209574551483,
+      "test_mse_per_seed": [
+        0.3944958746433258,
+        0.3963458240032196,
+        0.40111058950424194
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.4188590943813324
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.43054765462875366
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5643115043640137
+          }
+        ],
+        "avg": 0.4712394177913666
+      }
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.49391986926396686,
+      "test_mse_std": 0.015831822938167688,
+      "test_mse_cv": 0.0320534238919363,
+      "test_mse_per_seed": [
+        0.502124011516571,
+        0.47177648544311523,
+        0.5078591108322144
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5528786182403564
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.5760074257850647
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.44076889753341675
+          }
+        ],
+        "avg": 0.5232183138529459
+      }
+    }
+  },
+  "ETTm2": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.12108045319716136,
+      "test_mse_std": 0.0023783282575542586,
+      "test_mse_cv": 0.019642545057884014,
+      "test_mse_per_seed": [
+        0.11874931305646896,
+        0.12434578686952591,
+        0.1201462596654892
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5659838914871216
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.5422478914260864
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5607322454452515
+          }
+        ],
+        "avg": 0.5563213427861532
+      }
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.12246779352426529,
+      "test_mse_std": 0.00438693698902087,
+      "test_mse_cv": 0.03582114826092347,
+      "test_mse_per_seed": [
+        0.11810851842164993,
+        0.12847043573856354,
+        0.1208244264125824
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.523005485534668
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.5482986569404602
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5818163156509399
+          }
+        ],
+        "avg": 0.5510401527086893
+      }
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.12155637641747792,
+      "test_mse_std": 0.0013810590203536136,
+      "test_mse_cv": 0.011361469147537362,
+      "test_mse_per_seed": [
+        0.11961803585290909,
+        0.12273318320512772,
+        0.12231791019439697
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.410960853099823
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.4373443126678467
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5083509683609009
+          }
+        ],
+        "avg": 0.4522187113761902
+      }
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.12024080504973729,
+      "test_mse_std": 0.0012149167473383604,
+      "test_mse_cv": 0.010104030381664637,
+      "test_mse_per_seed": [
+        0.12195853888988495,
+        0.11934912204742432,
+        0.11941475421190262
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5627015829086304
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.578525722026825
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5250304937362671
+          }
+        ],
+        "avg": 0.5554192662239075
+      }
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.13877523442109427,
+      "test_mse_std": 0.011055070234293803,
+      "test_mse_cv": 0.0796616938203017,
+      "test_mse_per_seed": [
+        0.12634912133216858,
+        0.13677150011062622,
+        0.15320508182048798
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5381221175193787
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.6193260550498962
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5721343755722046
+          }
+        ],
+        "avg": 0.5765275160471598
+      }
+    }
+  },
+  "Weather": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.16085336109002432,
+      "test_mse_std": 0.0007431797642834034,
+      "test_mse_cv": 0.004620231490639914,
+      "test_mse_per_seed": [
+        0.15998028218746185,
+        0.16078317165374756,
+        0.16179662942886353
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5914392471313477
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.516383171081543
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5429453253746033
+          }
+        ],
+        "avg": 0.5502559145291647
+      }
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.17273412148157755,
+      "test_mse_std": 0.0033528512800093217,
+      "test_mse_cv": 0.01941047461411329,
+      "test_mse_per_seed": [
+        0.1749560683965683,
+        0.16799552738666534,
+        0.17525076866149902
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.6821513772010803
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.625710666179657
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5433353185653687
+          }
+        ],
+        "avg": 0.6170657873153687
+      }
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.1699669510126114,
+      "test_mse_std": 0.001586670658713356,
+      "test_mse_cv": 0.00933517162754556,
+      "test_mse_per_seed": [
+        0.1708894670009613,
+        0.17127713561058044,
+        0.16773425042629242
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.375617653131485
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.4995625913143158
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.3211539387702942
+          }
+        ],
+        "avg": 0.3987780610720317
+      }
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.16656955579916635,
+      "test_mse_std": 0.002227832033446215,
+      "test_mse_cv": 0.013374785222650901,
+      "test_mse_per_seed": [
+        0.16512899100780487,
+        0.16971644759178162,
+        0.1648632287979126
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.3997272551059723
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.36137861013412476
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.3907202482223511
+          }
+        ],
+        "avg": 0.38394203782081604
+      }
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.20317772030830383,
+      "test_mse_std": 0.004074763621687716,
+      "test_mse_cv": 0.020055169511227072,
+      "test_mse_per_seed": [
+        0.1986662894487381,
+        0.20853839814662933,
+        0.20232847332954407
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.36549609899520874
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.4984208941459656
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.5030889511108398
+          }
+        ],
+        "avg": 0.4556686480840047
+      }
+    }
+  },
+  "Electricity": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.18527705470720926,
+      "test_mse_std": 6.1883484088264e-05,
+      "test_mse_cv": 0.0003340051156688431,
+      "test_mse_per_seed": [
+        0.185342937707901,
+        0.1852940022945404,
+        0.1851942241191864
+      ]
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.20953640838464102,
+      "test_mse_std": 0.0021824612007222315,
+      "test_mse_cv": 0.010415665790719956,
+      "test_mse_per_seed": [
+        0.2076813131570816,
+        0.2126002311706543,
+        0.20832768082618713
+      ]
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.160057266553243,
+      "test_mse_std": 0.00027801120502109005,
+      "test_mse_cv": 0.0017369483498495815,
+      "test_mse_per_seed": [
+        0.16043004393577576,
+        0.15997910499572754,
+        0.1597626507282257
+      ]
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.16809411843617758,
+      "test_mse_std": 0.0002476421536472825,
+      "test_mse_cv": 0.001473235089669767,
+      "test_mse_per_seed": [
+        0.16774685680866241,
+        0.1683070808649063,
+        0.168228417634964
+      ]
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.3149534861246745,
+      "test_mse_std": 0.006519851053391807,
+      "test_mse_cv": 0.02070099662529508,
+      "test_mse_per_seed": [
+        0.32411080598831177,
+        0.31130772829055786,
+        0.3094419240951538
+      ]
+    }
+  },
+  "Traffic": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.6506451964378357,
+      "test_mse_std": 3.124932316126509e-05,
+      "test_mse_cv": 4.802820851110476e-05,
+      "test_mse_per_seed": [
+        0.6506800651550293,
+        0.650604248046875,
+        0.6506512761116028
+      ]
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.626276433467865,
+      "test_mse_std": 0.001638054458045168,
+      "test_mse_cv": 0.0026155454213322537,
+      "test_mse_per_seed": [
+        0.6239720582962036,
+        0.6272231340408325,
+        0.6276341080665588
+      ]
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.4888527989387512,
+      "test_mse_std": 0.000989588240071093,
+      "test_mse_cv": 0.0020243071988528785,
+      "test_mse_per_seed": [
+        0.4875304400920868,
+        0.48911717534065247,
+        0.4899107813835144
+      ]
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.5060910582542419,
+      "test_mse_std": 0.0019532593865581476,
+      "test_mse_cv": 0.0038595018716511297,
+      "test_mse_per_seed": [
+        0.505207359790802,
+        0.5042663812637329,
+        0.5087994337081909
+      ]
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.6965998212496439,
+      "test_mse_std": 0.011068284895335108,
+      "test_mse_cv": 0.01588901483706886,
+      "test_mse_per_seed": [
+        0.6958082318305969,
+        0.6834571361541748,
+        0.7105340957641602
+      ]
+    }
+  },
+  "Exchange": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.08744554469982783,
+      "test_mse_std": 0.002654847581206988,
+      "test_mse_cv": 0.03036000965309574,
+      "test_mse_per_seed": [
+        0.08734697848558426,
+        0.08424443751573563,
+        0.0907452180981636
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.5964889526367188
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.6545685529708862
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.662866473197937
+          }
+        ],
+        "avg": 0.6379746596018473
+      }
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.1890727678934733,
+      "test_mse_std": 0.0095784768141471,
+      "test_mse_cv": 0.05066026652523419,
+      "test_mse_per_seed": [
+        0.18853968381881714,
+        0.20106141269207,
+        0.17761720716953278
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.8284239768981934
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.8539069294929504
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.8321856260299683
+          }
+        ],
+        "avg": 0.838172177473704
+      }
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.1223595639069875,
+      "test_mse_std": 0.015103724456690643,
+      "test_mse_cv": 0.1234372203890155,
+      "test_mse_per_seed": [
+        0.104995496571064,
+        0.14181427657604218,
+        0.12026891857385635
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.748420000076294
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.668302595615387
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.6971916556358337
+          }
+        ],
+        "avg": 0.7046380837758383
+      }
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.1005382388830185,
+      "test_mse_std": 0.006984176818169931,
+      "test_mse_cv": 0.06946786512041837,
+      "test_mse_per_seed": [
+        0.10820480436086655,
+        0.09131181985139847,
+        0.10209809243679047
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.6076173782348633
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.7202003598213196
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.42130881547927856
+          }
+        ],
+        "avg": 0.5830421845118204
+      }
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 0.8359294136365255,
+      "test_mse_std": 0.0980074594445852,
+      "test_mse_cv": 0.11724370245356659,
+      "test_mse_per_seed": [
+        0.8384765386581421,
+        0.954669713973999,
+        0.7146419882774353
+      ],
+      "forecaster_seed_hungarian": {
+        "pairs": [
+          {
+            "seed_a": 11,
+            "seed_b": 22,
+            "hungarian": 0.7922174334526062
+          },
+          {
+            "seed_a": 11,
+            "seed_b": 42,
+            "hungarian": 0.5672711730003357
+          },
+          {
+            "seed_a": 22,
+            "seed_b": 42,
+            "hungarian": 0.7393757104873657
+          }
+        ],
+        "avg": 0.6996214389801025
+      }
+    }
+  },
+  "ILI": {
+    "dlinear": {
+      "n_seeds": 3,
+      "test_mse_mean": 4.280695120493571,
+      "test_mse_std": 0.07997423628486067,
+      "test_mse_cv": 0.01868253497007736,
+      "test_mse_per_seed": [
+        4.326862812042236,
+        4.1681952476501465,
+        4.34702730178833
+      ]
+    },
+    "patchtst": {
+      "n_seeds": 3,
+      "test_mse_mean": 5.633381525675456,
+      "test_mse_std": 0.15890197972095135,
+      "test_mse_cv": 0.02820721071291166,
+      "test_mse_per_seed": [
+        5.85625696182251,
+        5.497050762176514,
+        5.546836853027344
+      ]
+    },
+    "itransformer": {
+      "n_seeds": 3,
+      "test_mse_mean": 5.151209990183513,
+      "test_mse_std": 0.1870504563764083,
+      "test_mse_cv": 0.03631194549103299,
+      "test_mse_per_seed": [
+        4.887641429901123,
+        5.302501201629639,
+        5.263487339019775
+      ]
+    },
+    "nbeats": {
+      "n_seeds": 3,
+      "test_mse_mean": 4.772895812988281,
+      "test_mse_std": 0.1414466378613462,
+      "test_mse_cv": 0.029635391888595892,
+      "test_mse_per_seed": [
+        4.9091105461120605,
+        4.831654071807861,
+        4.577922821044922
+      ]
+    },
+    "timesnet": {
+      "n_seeds": 3,
+      "test_mse_mean": 8.733046213785807,
+      "test_mse_std": 0.24595507900859823,
+      "test_mse_cv": 0.028163721224827437,
+      "test_mse_per_seed": [
+        8.407791137695312,
+        8.78891372680664,
+        9.002433776855469
+      ]
+    }
+  }
+}

results/non_ett_analysis.json

@@ -0,0 +1,39 @@
+{
+  "non_ett_summary": {
+    "datasets": [
+      "Electricity",
+      "Exchange",
+      "ILI",
+      "Traffic",
+      "Weather"
+    ],
+    "winners": {
+      "Electricity": "TopK",
+      "Exchange": "TopK",
+      "ILI": "PCA",
+      "Traffic": "k-means",
+      "Weather": "Dense_AE_16"
+    },
+    "hungarian": {
+      "Electricity": 0.44465779066085814,
+      "Exchange": 0.5651660561561584,
+      "ILI": 0.22974909241311253,
+      "Traffic": 0.4109637692570686,
+      "Weather": 0.47962490022182463
+    },
+    "permutation_z": {
+      "Electricity": 757.5135975494625,
+      "Exchange": 82.73712523477309,
+      "ILI": 1.6061674652603422,
+      "Traffic": 609.9463464164547,
+      "Weather": 387.0661065737885
+    },
+    "lag1_vs_bf": {
+      "Electricity": 1.084284788469361,
+      "Exchange": 10.536282369266699,
+      "ILI": NaN,
+      "Traffic": 2.5188830138485483,
+      "Weather": 8.695609527438002
+    }
+  }
+}

results/selector_comparison.json

@@ -0,0 +1,101 @@
+{
+  "Electricity": {
+    "oracle": 0.17346416413784027,
+    "best_fixed": 0.17587409913539886,
+    "best_forecaster": "itransformer",
+    "uniform": 0.23147256672382355,
+    "lag1": 0.1739671230316162,
+    "learned": 0.18218165636062622,
+    "lag1_vs_bf_pct": 1.084284788469361,
+    "learned_vs_bf_pct": -3.5864048522411505,
+    "oracle_vs_bf_pct": 1.370261459422331
+  },
+  "Weather": {
+    "oracle": 0.15736375749111176,
+    "best_fixed": 0.17318648099899292,
+    "best_forecaster": "dlinear",
+    "uniform": 0.18943047523498535,
+    "lag1": 0.1581268608570099,
+    "learned": 0.1772189736366272,
+    "lag1_vs_bf_pct": 8.695609527438002,
+    "learned_vs_bf_pct": -2.3284107479831095,
+    "oracle_vs_bf_pct": 9.136234778032804
+  },
+  "Exchange": {
+    "oracle": 0.07413998246192932,
+    "best_fixed": 0.08332893252372742,
+    "best_forecaster": "dlinear",
+    "uniform": 0.20127807557582855,
+    "lag1": 0.07454916089773178,
+    "learned": 0.08332893252372742,
+    "lag1_vs_bf_pct": 10.536282369266699,
+    "learned_vs_bf_pct": 0.0,
+    "oracle_vs_bf_pct": 11.027322423915123
+  },
+  "ETTh1": {
+    "oracle": 0.3146692216396332,
+    "best_fixed": 0.3510667681694031,
+    "best_forecaster": "patchtst",
+    "uniform": 0.38277140259742737,
+    "lag1": 0.3187973201274872,
+    "learned": 0.3447442352771759,
+    "lag1_vs_bf_pct": 9.191826446627571,
+    "learned_vs_bf_pct": 1.8009488409271226,
+    "oracle_vs_bf_pct": 10.367699204217102
+  },
+  "ETTm1": {
+    "oracle": 0.28636232018470764,
+    "best_fixed": 0.33375412225723267,
+    "best_forecaster": "dlinear",
+    "uniform": 0.3884066641330719,
+    "lag1": 0.2898140251636505,
+    "learned": 0.33147087693214417,
+    "lag1_vs_bf_pct": 13.16540955251975,
+    "learned_vs_bf_pct": 0.6841099997946233,
+    "oracle_vs_bf_pct": 14.199615498980705
+  },
+  "Traffic": {
+    "oracle": 0.4852137565612793,
+    "best_fixed": 0.49937358498573303,
+    "best_forecaster": "itransformer",
+    "uniform": 0.6021071672439575,
+    "lag1": 0.48679494857788086,
+    "learned": 0.49927258491516113,
+    "lag1_vs_bf_pct": 2.5188830138485483,
+    "learned_vs_bf_pct": 0.02022535304401112,
+    "oracle_vs_bf_pct": 2.835518107121801
+  },
+  "ETTh2": {
+    "oracle": 0.11038471013307571,
+    "best_fixed": 0.13287261128425598,
+    "best_forecaster": "patchtst",
+    "uniform": 0.14123903214931488,
+    "lag1": 0.11149779707193375,
+    "learned": 0.12307851761579514,
+    "lag1_vs_bf_pct": 16.086696878858533,
+    "learned_vs_bf_pct": 7.371040257128855,
+    "oracle_vs_bf_pct": 16.924406718455792
+  },
+  "ILI": {
+    "oracle": 0.1587660014629364,
+    "best_fixed": 0.1587660014629364,
+    "best_forecaster": "dlinear",
+    "uniform": 2.7643885612487793,
+    "lag1": NaN,
+    "learned": 0.1587660014629364,
+    "lag1_vs_bf_pct": NaN,
+    "learned_vs_bf_pct": 0.0,
+    "oracle_vs_bf_pct": 0.0
+  },
+  "ETTm2": {
+    "oracle": 0.07642219215631485,
+    "best_fixed": 0.09322506189346313,
+    "best_forecaster": "patchtst",
+    "uniform": 0.09219101071357727,
+    "lag1": 0.07721394300460815,
+    "learned": 0.09247123450040817,
+    "lag1_vs_bf_pct": 17.174693761160878,
+    "learned_vs_bf_pct": 0.8086102360719591,
+    "oracle_vs_bf_pct": 18.02398346096082
+  }
+}

results/trivial_similarity_analysis.json

@@ -0,0 +1,200 @@
+{
+  "ETTh1": {
+    "n_windows": 3293,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.8918020009994507,
+    "centered_cosine_mean": -0.19645724520087243,
+    "centering_drop": 122.02924471807654,
+    "permutation_test": {
+      "observed": 0.9389805793762207,
+      "null_mean": 0.009991158929187805,
+      "null_std": 0.0030160671891238913,
+      "z_score": 308.0135030801108,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.9363326311111451,
+    "residual_cosine_mean": 0.8918020009994507,
+    "residual_minus_forecast": -0.044530630111694336,
+    "residual_energy_ratio": {
+      "mean": 0.608367661307428,
+      "min": 0.5904407924320196,
+      "max": 0.6642691893267721
+    }
+  },
+  "ETTh2": {
+    "n_windows": 3293,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.8110998034477234,
+    "centered_cosine_mean": -0.14715625569224358,
+    "centering_drop": 118.14280499967202,
+    "permutation_test": {
+      "observed": 0.8843784928321838,
+      "null_mean": 0.017277665303554388,
+      "null_std": 0.004014542862526737,
+      "z_score": 215.98992892129186,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.9639783203601837,
+    "residual_cosine_mean": 0.8110998034477234,
+    "residual_minus_forecast": -0.15287851691246035,
+    "residual_energy_ratio": {
+      "mean": 0.4133490398218841,
+      "min": 0.38290490605966515,
+      "max": 0.5025244281297173
+    }
+  },
+  "ETTm1": {
+    "n_windows": 13745,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.8472939252853393,
+    "centered_cosine_mean": -0.21075453273952008,
+    "centering_drop": 124.8738396971919,
+    "permutation_test": {
+      "observed": 0.8710768222808838,
+      "null_mean": 0.012391277011483908,
+      "null_std": 0.0019026290101919819,
+      "z_score": 451.31528041967334,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.9265291750431061,
+    "residual_cosine_mean": 0.8472939252853393,
+    "residual_minus_forecast": -0.07923524975776675,
+    "residual_energy_ratio": {
+      "mean": 0.5697619661409533,
+      "min": 0.5398345576692166,
+      "max": 0.6421444040215007
+    }
+  },
+  "ETTm2": {
+    "n_windows": 13745,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.8812912344932556,
+    "centered_cosine_mean": -0.20197361563332378,
+    "centering_drop": 122.91791949450841,
+    "permutation_test": {
+      "observed": 0.9115669131278992,
+      "null_mean": 0.009315435944590717,
+      "null_std": 0.0021711870751837305,
+      "z_score": 415.55676500467285,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.9887938916683197,
+    "residual_cosine_mean": 0.8812912344932556,
+    "residual_minus_forecast": -0.10750265717506402,
+    "residual_energy_ratio": {
+      "mean": 0.3175586952216497,
+      "min": 0.3140947661768296,
+      "max": 0.3271402465398939
+    }
+  },
+  "Electricity": {
+    "n_windows": 5071,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.7461866497993469,
+    "centered_cosine_mean": -0.1445682406425476,
+    "centering_drop": 119.37427327082611,
+    "permutation_test": {
+      "observed": 0.878393828868866,
+      "null_mean": 0.017970537007786332,
+      "null_std": 0.001135851943311021,
+      "z_score": 757.5135975494625,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.9314405381679535,
+    "residual_cosine_mean": 0.7461866497993469,
+    "residual_minus_forecast": -0.18525388836860657,
+    "residual_energy_ratio": {
+      "mean": 0.4439227623357736,
+      "min": 0.3912350098910411,
+      "max": 0.560962213004963
+    }
+  },
+  "Exchange": {
+    "n_windows": 1328,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.6275513231754303,
+    "centered_cosine_mean": -0.060707115940749644,
+    "centering_drop": 109.67364958033545,
+    "permutation_test": {
+      "observed": 0.7330744862556458,
+      "null_mean": 0.07461113901808858,
+      "null_std": 0.007958499227150034,
+      "z_score": 82.73712523477309,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.9441243588924408,
+    "residual_cosine_mean": 0.6275513231754303,
+    "residual_minus_forecast": -0.31657303571701056,
+    "residual_energy_ratio": {
+      "mean": 0.2691465167067837,
+      "min": 0.17191642010956823,
+      "max": 0.541848151695065
+    }
+  },
+  "ILI": {
+    "n_windows": 3,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.9247952044010163,
+    "centered_cosine_mean": -0.22498428113758565,
+    "centering_drop": 124.32801122528599,
+    "permutation_test": {
+      "observed": 0.9304211735725403,
+      "null_mean": 0.9027175793051719,
+      "null_std": 0.0172482601388504,
+      "z_score": 1.6061674652603422,
+      "p_value": 0.17
+    },
+    "forecast_cosine_mean": 0.6607760608196258,
+    "residual_cosine_mean": 0.9247952044010163,
+    "residual_minus_forecast": 0.26401914358139045,
+    "residual_energy_ratio": {
+      "mean": 0.8199637568741494,
+      "min": 0.7211203086695676,
+      "max": 1.0052227158550973
+    }
+  },
+  "Traffic": {
+    "n_windows": 3319,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.8356885731220245,
+    "centered_cosine_mean": -0.21413054782897234,
+    "centering_drop": 125.62324707025827,
+    "permutation_test": {
+      "observed": 0.8576543927192688,
+      "null_mean": 0.018838578751310706,
+      "null_std": 0.0013752288523345584,
+      "z_score": 609.9463464164547,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.8830878913402558,
+    "residual_cosine_mean": 0.8356885731220245,
+    "residual_minus_forecast": -0.04739931821823129,
+    "residual_energy_ratio": {
+      "mean": 0.6333390905086173,
+      "min": 0.5745549053691511,
+      "max": 0.6901400052114491
+    }
+  },
+  "Weather": {
+    "n_windows": 10349,
+    "n_forecasters": 5,
+    "raw_cosine_mean": 0.8085302352905274,
+    "centered_cosine_mean": -0.1999431726988405,
+    "centering_drop": 124.7292140691554,
+    "permutation_test": {
+      "observed": 0.8484342694282532,
+      "null_mean": 0.03273834900930524,
+      "null_std": 0.002107381417709968,
+      "z_score": 387.0661065737885,
+      "p_value": 0.0
+    },
+    "forecast_cosine_mean": 0.9201199829578399,
+    "residual_cosine_mean": 0.8085302352905274,
+    "residual_minus_forecast": -0.11158974766731256,
+    "residual_energy_ratio": {
+      "mean": 0.5103006143404778,
+      "min": 0.48715445410596137,
+      "max": 0.5550498567272207
+    }
+  }
+}

results/wilcoxon_full.json

@@ -0,0 +1,500 @@
+{
+  "ETTh1": {
+    "PCA": {
+      "p": 0.00030517578125,
+      "effect": 0.95,
+      "n": 15,
+      "mean_topk": 0.4811991792917251,
+      "mean_other": 0.41710500294963526,
+      "diff": 0.06409417634208986,
+      "holm_threshold": 0.008333333333333333,
+      "holm_sig": true
+    },
+    "Dense_AE_16": {
+      "p": 0.0008544921875,
+      "effect": 0.9,
+      "n": 15,
+      "mean_topk": 0.4811991792917251,
+      "mean_other": 0.4413383173942566,
+      "diff": 0.039860861897468514,
+      "holm_threshold": 0.01,
+      "holm_sig": true
+    },
+    "Dense_AE_64": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.4811991792917251,
+      "mean_other": 0.36336570166051385,
+      "diff": 0.11783347763121127,
+      "holm_threshold": 0.005555555555555556,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 0.5994873046875,
+      "effect": 0.16666666666666666,
+      "n": 15,
+      "mean_topk": 0.4811991792917251,
+      "mean_other": 0.476032989025116,
+      "diff": 0.005166190266609139,
+      "holm_threshold": 0.05,
+      "holm_sig": false
+    },
+    "ICA": {
+      "p": 0.00836181640625,
+      "effect": 0.75,
+      "n": 15,
+      "mean_topk": 0.4811991792917251,
+      "mean_other": 0.4479914022733768,
+      "diff": 0.03320777701834832,
+      "holm_threshold": 0.0125,
+      "holm_sig": true
+    },
+    "Spectral": {
+      "p": 0.389404296875,
+      "effect": -0.26666666666666666,
+      "n": 15,
+      "mean_topk": 0.4811991792917251,
+      "mean_other": 0.4942917775114377,
+      "diff": -0.013092598219712592,
+      "holm_threshold": 0.025,
+      "holm_sig": false
+    }
+  },
+  "ETTh2": {
+    "PCA": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.7116594993074735,
+      "mean_other": 0.5909992029331624,
+      "diff": 0.12066029637431108,
+      "holm_threshold": 0.005555555555555556,
+      "holm_sig": true
+    },
+    "Dense_AE_16": {
+      "p": 0.072998046875,
+      "effect": -0.5333333333333333,
+      "n": 15,
+      "mean_topk": 0.7116594993074735,
+      "mean_other": 0.7273528537899256,
+      "diff": -0.015693354482452126,
+      "holm_threshold": 0.016666666666666666,
+      "holm_sig": false
+    },
+    "Dense_AE_64": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.7116594993074735,
+      "mean_other": 0.4997276326020559,
+      "diff": 0.21193186670541764,
+      "holm_threshold": 0.00625,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 0.0001220703125,
+      "effect": -0.9833333333333333,
+      "n": 15,
+      "mean_topk": 0.7116594993074735,
+      "mean_other": 0.7472399912277858,
+      "diff": -0.03558049192031232,
+      "holm_threshold": 0.008333333333333333,
+      "holm_sig": true
+    },
+    "ICA": {
+      "p": 0.1353759765625,
+      "effect": 0.45,
+      "n": 15,
+      "mean_topk": 0.7116594993074735,
+      "mean_other": 0.7010225590815147,
+      "diff": 0.010636940225958846,
+      "holm_threshold": 0.025,
+      "holm_sig": false
+    },
+    "Spectral": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.7116594993074735,
+      "mean_other": 0.6587891900539398,
+      "diff": 0.052870309253533754,
+      "holm_threshold": 0.016666666666666666,
+      "holm_sig": true
+    }
+  },
+  "ETTm1": {
+    "PCA": {
+      "p": 0.00030517578125,
+      "effect": 0.95,
+      "n": 15,
+      "mean_topk": 0.655919008453687,
+      "mean_other": 0.5414499825487534,
+      "diff": 0.11446902590493357,
+      "holm_threshold": 0.01,
+      "holm_sig": true
+    },
+    "Dense_AE_16": {
+      "p": 0.12054443359375,
+      "effect": -0.4666666666666667,
+      "n": 15,
+      "mean_topk": 0.655919008453687,
+      "mean_other": 0.6701205569505692,
+      "diff": -0.014201548496882155,
+      "holm_threshold": 0.025,
+      "holm_sig": false
+    },
+    "Dense_AE_64": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.655919008453687,
+      "mean_other": 0.5176160377884904,
+      "diff": 0.1383029706651966,
+      "holm_threshold": 0.0071428571428571435,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 0.1876220703125,
+      "effect": 0.4,
+      "n": 15,
+      "mean_topk": 0.655919008453687,
+      "mean_other": 0.6482138058543204,
+      "diff": 0.0077052025993665785,
+      "holm_threshold": 0.025,
+      "holm_sig": false
+    },
+    "ICA": {
+      "p": 0.00836181640625,
+      "effect": 0.75,
+      "n": 15,
+      "mean_topk": 0.655919008453687,
+      "mean_other": 0.6264328748111924,
+      "diff": 0.02948613364249464,
+      "holm_threshold": 0.016666666666666666,
+      "holm_sig": true
+    }
+  },
+  "ETTm2": {
+    "PCA": {
+      "p": 0.97796630859375,
+      "effect": 0.016666666666666666,
+      "n": 15,
+      "mean_topk": 0.6153921738266945,
+      "mean_other": 0.6176143797238668,
+      "diff": -0.002222205897172258,
+      "holm_threshold": 0.05,
+      "holm_sig": false
+    },
+    "Dense_AE_16": {
+      "p": 6.103515625e-05,
+      "effect": -1.0,
+      "n": 15,
+      "mean_topk": 0.6153921738266945,
+      "mean_other": 0.7307655590027571,
+      "diff": -0.11537338517606255,
+      "holm_threshold": 0.005555555555555556,
+      "holm_sig": true
+    },
+    "Dense_AE_64": {
+      "p": 0.0008544921875,
+      "effect": 0.9,
+      "n": 15,
+      "mean_topk": 0.6153921738266945,
+      "mean_other": 0.5603966174523035,
+      "diff": 0.05499555637439102,
+      "holm_threshold": 0.025,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 6.103515625e-05,
+      "effect": -1.0,
+      "n": 15,
+      "mean_topk": 0.6153921738266945,
+      "mean_other": 0.6921540379524231,
+      "diff": -0.07676186412572861,
+      "holm_threshold": 0.005555555555555556,
+      "holm_sig": true
+    },
+    "ICA": {
+      "p": 6.103515625e-05,
+      "effect": -1.0,
+      "n": 15,
+      "mean_topk": 0.6153921738266945,
+      "mean_other": 0.6943691419064998,
+      "diff": -0.07897696807980525,
+      "holm_threshold": 0.005555555555555556,
+      "holm_sig": true
+    }
+  },
+  "Electricity": {
+    "PCA": {
+      "p": 0.00030517578125,
+      "effect": 0.95,
+      "n": 15,
+      "mean_topk": 0.5581442653139432,
+      "mean_other": 0.5055057212710381,
+      "diff": 0.05263854404290513,
+      "holm_threshold": 0.0125,
+      "holm_sig": true
+    },
+    "Dense_AE_16": {
+      "p": 0.0067138671875,
+      "effect": 0.7666666666666667,
+      "n": 15,
+      "mean_topk": 0.5581442653139432,
+      "mean_other": 0.5306520505746205,
+      "diff": 0.02749221473932273,
+      "holm_threshold": 0.0125,
+      "holm_sig": true
+    },
+    "Dense_AE_64": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.5581442653139432,
+      "mean_other": 0.43628965241213646,
+      "diff": 0.12185461290180677,
+      "holm_threshold": 0.008333333333333333,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.5581442653139432,
+      "mean_other": 0.490592661301295,
+      "diff": 0.06755160401264826,
+      "holm_threshold": 0.00625,
+      "holm_sig": true
+    },
+    "ICA": {
+      "p": 0.00201416015625,
+      "effect": 0.85,
+      "n": 15,
+      "mean_topk": 0.5581442653139432,
+      "mean_other": 0.5119286266962687,
+      "diff": 0.046215638617674526,
+      "holm_threshold": 0.01,
+      "holm_sig": true
+    }
+  },
+  "Exchange": {
+    "PCA": {
+      "p": 0.0001220703125,
+      "effect": 0.9833333333333333,
+      "n": 15,
+      "mean_topk": 0.898530842065811,
+      "mean_other": 0.8362344759702682,
+      "diff": 0.06229636609554279,
+      "holm_threshold": 0.0071428571428571435,
+      "holm_sig": true
+    },
+    "Dense_AE_16": {
+      "p": 0.0001220703125,
+      "effect": 0.9833333333333333,
+      "n": 15,
+      "mean_topk": 0.898530842065811,
+      "mean_other": 0.8692389285564422,
+      "diff": 0.029291913509368772,
+      "holm_threshold": 0.0071428571428571435,
+      "holm_sig": true
+    },
+    "Dense_AE_64": {
+      "p": 0.00030517578125,
+      "effect": 0.95,
+      "n": 15,
+      "mean_topk": 0.898530842065811,
+      "mean_other": 0.8435728583733242,
+      "diff": 0.054957983692486834,
+      "holm_threshold": 0.016666666666666666,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 0.03533935546875,
+      "effect": 0.6166666666666667,
+      "n": 15,
+      "mean_topk": 0.898530842065811,
+      "mean_other": 0.8858832160631815,
+      "diff": 0.012647626002629475,
+      "holm_threshold": 0.0125,
+      "holm_sig": false
+    },
+    "ICA": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.898530842065811,
+      "mean_other": 0.8435129668315253,
+      "diff": 0.055017875234285696,
+      "holm_threshold": 0.00625,
+      "holm_sig": true
+    },
+    "Spectral": {
+      "p": 0.45428466796875,
+      "effect": 0.23333333333333334,
+      "n": 15,
+      "mean_topk": 0.898530842065811,
+      "mean_other": 0.8962228451172509,
+      "diff": 0.002307996948560098,
+      "holm_threshold": 0.05,
+      "holm_sig": false
+    }
+  },
+  "ILI": {
+    "PCA": {
+      "p": 0.2623135752307827,
+      "effect": -0.4642857142857143,
+      "n": 15,
+      "mean_topk": 0.9615951037406921,
+      "mean_other": 0.9615951073169707,
+      "diff": -3.5762786065873797e-09,
+      "holm_threshold": 0.016666666666666666,
+      "holm_sig": false
+    },
+    "Dense_AE_16": {
+      "p": 1.0,
+      "effect": 0.0,
+      "n": 15,
+      "mean_topk": 0.9615951037406921,
+      "mean_other": 0.961595103740692,
+      "diff": 1.1102230246251565e-16,
+      "holm_threshold": 0.05,
+      "holm_sig": false
+    },
+    "Dense_AE_64": {
+      "p": 0.04216819709715596,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.9615951037406921,
+      "mean_other": 0.9615950993696849,
+      "diff": 4.371007222836454e-09,
+      "holm_threshold": 0.05,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 0.043114446783075355,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.9615951037406921,
+      "mean_other": 0.9615950949986776,
+      "diff": 8.74201455669521e-09,
+      "holm_threshold": 0.016666666666666666,
+      "holm_sig": false
+    },
+    "ICA": {
+      "p": 0.1411161381713362,
+      "effect": 0.6666666666666666,
+      "n": 15,
+      "mean_topk": 0.9615951037406921,
+      "mean_other": 0.9615951005617777,
+      "diff": 3.1789144649962964e-09,
+      "holm_threshold": 0.05,
+      "holm_sig": false
+    }
+  },
+  "Traffic": {
+    "PCA": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.30444055080413823,
+      "mean_other": 0.19156031923989456,
+      "diff": 0.11288023156424368,
+      "holm_threshold": 0.00625,
+      "holm_sig": true
+    },
+    "Dense_AE_16": {
+      "p": 0.0006103515625,
+      "effect": 0.9166666666666666,
+      "n": 15,
+      "mean_topk": 0.30444055080413823,
+      "mean_other": 0.27530888666709263,
+      "diff": 0.0291316641370456,
+      "holm_threshold": 0.008333333333333333,
+      "holm_sig": true
+    },
+    "Dense_AE_64": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.30444055080413823,
+      "mean_other": 0.2514161698520183,
+      "diff": 0.05302438095211992,
+      "holm_threshold": 0.01,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 0.00537109375,
+      "effect": -0.7833333333333333,
+      "n": 15,
+      "mean_topk": 0.30444055080413823,
+      "mean_other": 0.3265250971913337,
+      "diff": -0.02208454638719548,
+      "holm_threshold": 0.01,
+      "holm_sig": true
+    },
+    "ICA": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.30444055080413823,
+      "mean_other": 0.2133237272611586,
+      "diff": 0.09111682354297962,
+      "holm_threshold": 0.0071428571428571435,
+      "holm_sig": true
+    }
+  },
+  "Weather": {
+    "PCA": {
+      "p": 0.33026123046875,
+      "effect": -0.3,
+      "n": 15,
+      "mean_topk": 0.6523081323504448,
+      "mean_other": 0.6775962777932484,
+      "diff": -0.025288145442803556,
+      "holm_threshold": 0.025,
+      "holm_sig": false
+    },
+    "Dense_AE_16": {
+      "p": 6.103515625e-05,
+      "effect": -1.0,
+      "n": 15,
+      "mean_topk": 0.6523081323504448,
+      "mean_other": 0.7185343440373739,
+      "diff": -0.0662262116869291,
+      "holm_threshold": 0.00625,
+      "holm_sig": true
+    },
+    "Dense_AE_64": {
+      "p": 6.103515625e-05,
+      "effect": 1.0,
+      "n": 15,
+      "mean_topk": 0.6523081323504448,
+      "mean_other": 0.5670199275016785,
+      "diff": 0.08528820484876631,
+      "holm_threshold": 0.0125,
+      "holm_sig": true
+    },
+    "k-means": {
+      "p": 6.103515625e-05,
+      "effect": -1.0,
+      "n": 15,
+      "mean_topk": 0.6523081323504448,
+      "mean_other": 0.6846980163455009,
+      "diff": -0.032389883995056046,
+      "holm_threshold": 0.0071428571428571435,
+      "holm_sig": true
+    },
+    "ICA": {
+      "p": 0.0001220703125,
+      "effect": -0.9833333333333333,
+      "n": 15,
+      "mean_topk": 0.6523081323504448,
+      "mean_other": 0.6845697929337622,
+      "diff": -0.032261660583317364,
+      "holm_threshold": 0.008333333333333333,
+      "holm_sig": true
+    }
+  }
+}