| --- |
| 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. |
| |
