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QUALITY_REPORT.md

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+# Corpus QA summary
+
+- datasets: **39**
+- total series: **1,399,719**
+- total points: **8,690,897,054**
+- passed hard fingerprint checks: **39/39**
+
+| dataset | source | domain | series (exp) | channels | max_len (exp) | nan% | hard? |
+|---|---|---|---|---|---|---|---|
+| alibaba_cluster_trace_2018 | lotsa | Cloud | 58409 (58409) | ['2'] (2) | 1728 (1728) | 0.0 | OK |
+| australian_electricity | chronos | Energy | 5 (5) | ['1'] (1) | 232272 (232272) | 0.0 | OK |
+| bdg2_bull | lotsa | Energy | 41 (41) | ['1'] (1) | 17544 (12280) * | 0.0 | OK |
+| bdg2_fox | lotsa | Energy | 135 (135) | ['1'] (1) | 17544 (12280) * | 0.0 | OK |
+| bdg2_panther | lotsa | Energy | 105 (105) | ['1'] (1) | 8760 (6132) * | 0.0 | OK |
+| buildings_900k | lotsa | Energy | 100000 (100000) | ['1'] (1) | 8761 (8759) * | 0.0 | OK |
+| china_air_quality | lotsa | Climate | 437 (437) | ['6'] (6) | 397335 (397335) | 51.2 | OK |
+| cmip6_2000 | lotsa | Climate | 8192 (8192) | ['22'] (22) | 7300 (7300) | 0.0 | OK |
+| era5_1989 | lotsa | Climate | 8192 (8192) | ['15'] (15) | 8736 (8736) | 0.0 | OK |
+| era5_1990 | lotsa | Climate | 8192 (8192) | ['15'] (15) | 8736 (8736) | 0.0 | OK |
+| era5_1991 | lotsa | Climate | 8192 (8192) | ['15'] (15) | 8736 (8736) | 0.0 | OK |
+| kernel_synth_1m | chronos | Synthetic | 1000000 (1000000) | ['1'] (1) | 1024 (1024) | 0.0 | OK |
+| mexico_city_bikes | chronos | Traffic | 494 (494) | ['1'] (1) | 104449 (104449) | 0.0 | OK |
+| monash_m3_monthly | lotsa | Econ/Fin | 1428 (1428) | ['1'] (1) | 126 (126) | 0.0 | OK |
+| nn5_weekly | lotsa | Econ/Fin | 111 (111) | ['1'] (1) | 105 (105) | 0.0 | OK |
+| pems04 | lotsa | Traffic | 307 (307) | ['3'] (3) | 16992 (16992) | 0.0 | OK |
+| pems07 | lotsa | Traffic | 883 (883) | ['1'] (1) | 28224 (28224) | 0.0 | OK |
+| pems08 | lotsa | Traffic | 170 (170) | ['3'] (3) | 17856 (17856) | 0.0 | OK |
+| project_tycho | lotsa | Health | 1258 (1258) | ['1'] (1) | 3854 (3854) | 14.16 | OK |
+| q_traffic | lotsa | Traffic | 45148 (45148) | ['1'] (1) | 5856 (5856) | 0.0 | OK |
+| residential_load_power | lotsa | Energy | 271 (271) | ['3'] (3) | 614880 (614880) | 3.11 | OK |
+| residential_pv_power | lotsa | Energy | 233 (233) | ['3'] (3) | 614880 (614880) | 3.14 | OK |
+| subseasonal | lotsa | Climate | 862 (862) | ['4'] (4) | 16470 (16470) | 0.0 | OK |
+| subseasonal_precip | lotsa | Climate | 862 (862) | ['1'] (1) | 11323 (11323) | 0.0 | OK |
+| syn_anomaly | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 10000 (10000) | 0.0 | OK |
+| syn_forecastpfn | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 10000 (10000) | 0.0 | OK |
+| syn_gp | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 2048 (2048) | 0.0 | OK |
+| syn_ou | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 10000 (10000) | 0.0 | OK |
+| syn_sawtooth | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 10000 (10000) | 0.0 | OK |
+| syn_sinewave | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 10000 (10000) | 0.0 | OK |
+| syn_spikes | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 10000 (10000) | 0.0 | OK |
+| syn_step | tempopfn | Synthetic | 5000 (5000) | ['1'] (1) | 10000 (10000) | 0.0 | OK |
+| taxi_1h | chronos | Traffic | 2428 (2428) | ['1'] (1) | 744 (744) | 0.0 | OK |
+| taxi_30min | chronos | Traffic | 2428 (2428) | ['1'] (1) | 1488 (1488) | 0.0 | OK |
+| uber_tlc_hourly | chronos | Traffic | 262 (262) | ['1'] (1) | 4344 (4344) | 0.0 | OK |
+| weatherbench_daily | chronos | Climate | 10000 (10000) | ['1'] (1) | 14610 (14610) | 0.0 | OK |
+| wiki_daily_100k | chronos | Web | 100000 (100000) | ['1'] (1) | 2741 (2741) | 0.0 | OK |
+| wind_farms_daily | chronos | Energy | 337 (337) | ['1'] (1) | 366 (366) | 16.78 | OK |
+| wind_farms_hourly | chronos | Energy | 337 (337) | ['1'] (1) | 8784 (6148) * | 17.02 | OK |
+
+\* max_length differs from Table 5 (current upstream snapshot has longer raw series); soft check only.

README.md

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+---
+license: other
+pretty_name: TS-ICL Pretraining Corpus (reconstruction)
+task_categories:
+- time-series-forecasting
+tags:
+- time-series
+- foundation-model
+- pretraining
+- ts-icl
+size_categories:
+- 1M<n<10M
+configs:
+- config_name: all
+  data_files:
+  - split: train
+    path: "data/*/*.parquet"
+- config_name: bdg2_bull
+  data_files:
+  - split: train
+    path: data/bdg2_bull/*.parquet
+- config_name: bdg2_fox
+  data_files:
+  - split: train
+    path: data/bdg2_fox/*.parquet
+- config_name: bdg2_panther
+  data_files:
+  - split: train
+    path: data/bdg2_panther/*.parquet
+- config_name: buildings_900k
+  data_files:
+  - split: train
+    path: data/buildings_900k/*.parquet
+- config_name: residential_load_power
+  data_files:
+  - split: train
+    path: data/residential_load_power/*.parquet
+- config_name: residential_pv_power
+  data_files:
+  - split: train
+    path: data/residential_pv_power/*.parquet
+- config_name: china_air_quality
+  data_files:
+  - split: train
+    path: data/china_air_quality/*.parquet
+- config_name: cmip6_2000
+  data_files:
+  - split: train
+    path: data/cmip6_2000/*.parquet
+- config_name: era5_1989
+  data_files:
+  - split: train
+    path: data/era5_1989/*.parquet
+- config_name: era5_1990
+  data_files:
+  - split: train
+    path: data/era5_1990/*.parquet
+- config_name: era5_1991
+  data_files:
+  - split: train
+    path: data/era5_1991/*.parquet
+- config_name: subseasonal
+  data_files:
+  - split: train
+    path: data/subseasonal/*.parquet
+- config_name: subseasonal_precip
+  data_files:
+  - split: train
+    path: data/subseasonal_precip/*.parquet
+- config_name: pems04
+  data_files:
+  - split: train
+    path: data/pems04/*.parquet
+- config_name: pems07
+  data_files:
+  - split: train
+    path: data/pems07/*.parquet
+- config_name: pems08
+  data_files:
+  - split: train
+    path: data/pems08/*.parquet
+- config_name: q_traffic
+  data_files:
+  - split: train
+    path: data/q_traffic/*.parquet
+- config_name: alibaba_cluster_trace_2018
+  data_files:
+  - split: train
+    path: data/alibaba_cluster_trace_2018/*.parquet
+- config_name: monash_m3_monthly
+  data_files:
+  - split: train
+    path: data/monash_m3_monthly/*.parquet
+- config_name: nn5_weekly
+  data_files:
+  - split: train
+    path: data/nn5_weekly/*.parquet
+- config_name: project_tycho
+  data_files:
+  - split: train
+    path: data/project_tycho/*.parquet
+- config_name: australian_electricity
+  data_files:
+  - split: train
+    path: data/australian_electricity/*.parquet
+- config_name: wind_farms_hourly
+  data_files:
+  - split: train
+    path: data/wind_farms_hourly/*.parquet
+- config_name: wind_farms_daily
+  data_files:
+  - split: train
+    path: data/wind_farms_daily/*.parquet
+- config_name: weatherbench_daily
+  data_files:
+  - split: train
+    path: data/weatherbench_daily/*.parquet
+- config_name: mexico_city_bikes
+  data_files:
+  - split: train
+    path: data/mexico_city_bikes/*.parquet
+- config_name: taxi_30min
+  data_files:
+  - split: train
+    path: data/taxi_30min/*.parquet
+- config_name: taxi_1h
+  data_files:
+  - split: train
+    path: data/taxi_1h/*.parquet
+- config_name: uber_tlc_hourly
+  data_files:
+  - split: train
+    path: data/uber_tlc_hourly/*.parquet
+- config_name: wiki_daily_100k
+  data_files:
+  - split: train
+    path: data/wiki_daily_100k/*.parquet
+- config_name: kernel_synth_1m
+  data_files:
+  - split: train
+    path: data/kernel_synth_1m/*.parquet
+- config_name: syn_anomaly
+  data_files:
+  - split: train
+    path: data/syn_anomaly/*.parquet
+- config_name: syn_forecastpfn
+  data_files:
+  - split: train
+    path: data/syn_forecastpfn/*.parquet
+- config_name: syn_gp
+  data_files:
+  - split: train
+    path: data/syn_gp/*.parquet
+- config_name: syn_sawtooth
+  data_files:
+  - split: train
+    path: data/syn_sawtooth/*.parquet
+- config_name: syn_sinewave
+  data_files:
+  - split: train
+    path: data/syn_sinewave/*.parquet
+- config_name: syn_spikes
+  data_files:
+  - split: train
+    path: data/syn_spikes/*.parquet
+- config_name: syn_step
+  data_files:
+  - split: train
+    path: data/syn_step/*.parquet
+- config_name: syn_ou
+  data_files:
+  - split: train
+    path: data/syn_ou/*.parquet
+---
+
+# TS-ICL Pretraining Corpus (community reconstruction)
+
+A unified, cleaned reconstruction of the **univariate pretraining corpus** described in
+Table 5 of *TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via
+In-Context Learning* (Le Naour, Nabil & Petralia, EDF R&D; arXiv:2606.05878). The TS-ICL
+authors did not release their pretraining data pipeline, so this corpus is rebuilt from the
+named upstream sources (LOTSA, Chronos, and the TempoPFN synthetic generators) and
+normalized into a single schema.
+
+> **Not an official release.** Each subset retains the license of its upstream source
+> (see *Licensing*). This is a derivative dataset for research reproducibility.
+
+## At a glance
+
+- **39 datasets**, **1,399,719 series**, **8,690,897,054 observations**
+- Sources: LOTSA (21), Chronos (10), TempoPFN synthetic (8)
+- Single consistent schema, sharded Parquet (zstd)
+
+## Repository layout
+
+```
+data/<config>/*.parquet     # one folder per dataset (= HF config)
+meta/datasets.yaml          # build registry (sources, fingerprints, subsampling)
+meta/reports/<config>.json  # per-dataset build/QA report
+meta/qa.json                # aggregated QA
+QUALITY_REPORT.md           # human-readable QA table
+```
+
+## Schema
+
+Every row is one (possibly multivariate) time series:
+
+| field | type | description |
+|---|---|---|
+| `item_id` | string | unique id within a dataset |
+| `dataset` | string | dataset name (= config name) |
+| `domain` | string | Energy / Climate / Traffic / Cloud / Web / Econ-Fin / Health / Synthetic |
+| `source` | string | lotsa / chronos / chronos_extra / tempopfn |
+| `freq` | string | pandas offset alias (null for synthetic) |
+| `start` | timestamp[s] | start time (null for synthetic) |
+| `target` | list<list<float32>> | values as **[num_channels, length]** (univariate = [1, length]) |
+| `num_channels` | int32 | number of channels |
+| `length` | int32 | series length |
+| `weight` | float32 | Table-5 sampling coefficient (training-sampler metadata) |
+
+## How to load
+
+```python
+from datasets import load_dataset
+
+# one dataset (config)
+ds = load_dataset("<repo_id>", "nn5_weekly", split="train")
+
+# everything, streamed
+ds = load_dataset("<repo_id>", "all", split="train", streaming=True)
+```
+
+## Datasets (provenance)
+
+| config | paper name | source | upstream location | domain | freq | weight | series |
+|---|---|---|---|---|---|---|---|
+| `bdg2_bull` | BDG-2 Bull | lotsa | `bull` | Energy | H | 25 | 41 |
+| `bdg2_fox` | BDG-2 Fox | lotsa | `bdg-2_fox` | Energy | H | 5 | 135 |
+| `bdg2_panther` | BDG-2 Panther | lotsa | `bdg-2_panther` | Energy | H | 2.5 | 105 |
+| `buildings_900k` | BuildingsBench900k | lotsa | `buildings_900k` | Energy | H | 0.02048 | 100,000 |
+| `residential_load_power` | Residential Load Power | lotsa | `residential_load_power` | Energy | 1T | 1.2 | 271 |
+| `residential_pv_power` | Residential PV Power | lotsa | `residential_pv_power` | Energy | 1T | 1.5 | 233 |
+| `china_air_quality` | China Air Quality | lotsa | `china_air_quality` | Climate | H | 0.3 | 437 |
+| `cmip6_2000` | CMIP6 2000 | lotsa | `cmip6_2000` | Climate | 6H | 0.057 | 8,192 |
+| `era5_1989` | ERA5 1989 | lotsa | `era5_1989` | Climate | H | 0.085 | 8,192 |
+| `era5_1990` | ERA5 1990 | lotsa | `era5_1990` | Climate | H | 0.085 | 8,192 |
+| `era5_1991` | ERA5 1991 | lotsa | `era5_1991` | Climate | H | 0.085 | 8,192 |
+| `subseasonal` | Subseasonal | lotsa | `subseasonal` | Climate | 1D | 0.3 | 862 |
+| `subseasonal_precip` | Subseasonal Precipitation | lotsa | `subseasonal_precip` | Climate | 1D | 1.2 | 862 |
+| `pems04` | PEMS04 | lotsa | `PEMS04` | Traffic | 5T | 1.2 | 307 |
+| `pems07` | PEMS07 | lotsa | `PEMS07` | Traffic | 5T | 1.2 | 883 |
+| `pems08` | PEMS08 | lotsa | `PEMS08` | Traffic | 5T | 2.1 | 170 |
+| `q_traffic` | Q-TRAFFIC | lotsa | `Q-TRAFFIC` | Traffic | 15T | 0.024 | 45,148 |
+| `alibaba_cluster_trace_2018` | Alibaba Cluster Trace 2018 | lotsa | `alibaba_cluster_trace_2018` | Cloud | 5T | 0.009 | 58,409 |
+| `monash_m3_monthly` | Monash M3 Monthly | lotsa | `monash_m3_monthly` | Econ/Fin | M | 0.72 | 1,428 |
+| `nn5_weekly` | NN5 Weekly | lotsa | `nn5_weekly` | Econ/Fin | W | 5 | 111 |
+| `project_tycho` | Project Tycho | lotsa | `project_tycho` | Health | W | 0.21 | 1,258 |
+| `australian_electricity` | Australian Electricity | chronos | `monash_australian_electricity` | Energy | 30T | 220 | 5 |
+| `wind_farms_hourly` | Wind Farms H | chronos | `wind_farms_hourly` | Energy | H | 4 | 337 |
+| `wind_farms_daily` | Wind Farms D | chronos | `wind_farms_daily` | Energy | D | 2 | 337 |
+| `weatherbench_daily` | Weatherbench daily | chronos | `weatherbench_daily` | Climate | 1D | 0.1024 | 10,000 |
+| `mexico_city_bikes` | Mexico City Bikes | chronos | `mexico_city_bikes` | Traffic | H | 2.5 | 494 |
+| `taxi_30min` | Taxi (30 Min.) | chronos | `taxi_30min` | Traffic | 30T | 0.88 | 2,428 |
+| `taxi_1h` | Taxi (Hourly) | chronos | `taxi_1h` | Traffic | H | 0.88 | 2,428 |
+| `uber_tlc_hourly` | Uber TLC (Hourly) | chronos | `uber_tlc_hourly` | Traffic | H | 4 | 262 |
+| `wiki_daily_100k` | Wiki Daily | chronos | `wiki_daily_100k` | Web | D | 0.00512 | 100,000 |
+| `kernel_synth_1m` | Kernel Synth 1M | chronos | `training_corpus/kernel_synth_1m` | Synthetic | None | 0.001024 | 1,000,000 |
+| `syn_anomaly` | Anomaly | tempopfn | `AnomalyGenerator` | Synthetic | None | 0.0256 | 5,000 |
+| `syn_forecastpfn` | ForecastPFN | tempopfn | `ForecastPFNGenerator` | Synthetic | None | 1 | 5,000 |
+| `syn_gp` | GP | tempopfn | `GPGenerator` | Synthetic | None | 0.4096 | 5,000 |
+| `syn_sawtooth` | Sawtooth | tempopfn | `SawToothGenerator` | Synthetic | None | 0.0512 | 5,000 |
+| `syn_sinewave` | Sinewave | tempopfn | `SineWaveGenerator` | Synthetic | None | 0.1024 | 5,000 |
+| `syn_spikes` | Spikes | tempopfn | `SpikesGenerator` | Synthetic | None | 0.0256 | 5,000 |
+| `syn_step` | Step | tempopfn | `StepGenerator` | Synthetic | None | 0.0512 | 5,000 |
+| `syn_ou` | OU | tempopfn | `OrnsteinUhlenbeckProcessGenerator` | Synthetic | None | 0.4096 | 5,000 |
+
+## Reconstruction methodology
+
+- **LOTSA** subsets: from `Salesforce/lotsa_data` (Arrow); targets passed through.
+- **Chronos** subsets: from `autogluon/chronos_datasets`; per-series `timestamp` + value
+  columns converted to `start` / `freq` (inferred) / `target`. KernelSynth-1M from
+  `training_corpus/kernel_synth_1m`.
+- **Spanish Weather**: from `autogluon/chronos_datasets_extra` (Kaggle-backed; needs Kaggle
+  credentials), reshaped to 5 cities x {temp, pressure, humidity}. *(Omitted unless built
+  with Kaggle creds.)*
+- **Synthetic** (`syn_*`): regenerated with the open-source `automl/TempoPFN` generators
+  (Anomaly, ForecastPFN, GP, Sawtooth, SineWave, Spikes, Step, OU), 5,000 series each,
+  fixed per-dataset seeds. Exact paper samples are not recoverable; these are statistically
+  equivalent and reproducible from the documented seeds.
+- **Paper-faithful downsampling** (seeded): `buildings_900k` -> 100k of ~1.8M series;
+  `weatherbench_daily` -> 10k; `era5_*` -> 15 of 45 channels; `cmip6_2000` -> 22 of 53.
+- **Cleaning**: float32; inf -> NaN (missing values preserved as NaN); empty/all-NaN series
+  dropped; frequency aliases normalized.
+- **Validation**: #series and #channels checked against Table 5 (hard); max_length is
+  informational (current upstream snapshots sometimes have longer raw series than Table 5).
+
+### Known deviations from the paper
+
+- **`syn_gp` uses length 2048** instead of 10,000. Exact GP at length 10,000 is
+  computationally infeasible (Cholesky fails -> `symeig` on 10k x 10k matrices, ~minutes
+  each; ~24 h for 5,000 series). 2048 is a standard GP/KernelSynth prior length.
+- A few datasets (e.g. BDG-2, wind_farms_hourly) have **longer max_length** than Table 5
+  because the current upstream snapshot contains longer raw series. Values are unmodified.
+
+## Licensing
+
+Derivative reconstruction; each subset is governed by its **upstream license**:
+- LOTSA subsets: see `Salesforce/lotsa_data` (per-dataset licenses).
+- Chronos subsets: see `autogluon/chronos_datasets`.
+- Spanish Weather: Kaggle "energy-consumption-generation-prices-and-weather".
+- Synthetic `syn_*`: generated with `automl/TempoPFN` (Apache-2.0).
+
+Users must comply with the original licenses and cite the original dataset authors.
+
+## Citations
+
+```bibtex
+@article{lenaour2026tsicl,
+  title  = {TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning},
+  author = {Le Naour, Etienne and Nabil, Tahar and Petralia, Adrien},
+  journal= {arXiv preprint arXiv:2606.05878},
+  year   = {2026}
+}
+@article{woo2024moirai, title={Unified Training of Universal Time Series Forecasting Transformers (LOTSA)}, author={Woo, Gerald and others}, year={2024}}
+@article{ansari2024chronos, title={Chronos: Learning the Language of Time Series}, author={Ansari, Abdul Fatir and others}, journal={arXiv:2403.07815}, year={2024}}
+@misc{moroshan2025tempopfn, title={TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-Shot Time Series Forecasting}, author={Moroshan, Vladyslav and Siems, Julien and Zela, Arber and Carstensen, Timur and Hutter, Frank}, eprint={2510.25502}, year={2025}}
+```

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meta/datasets.yaml

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+# TS-ICL pretraining corpus registry (paper Table 5: 40 univariate datasets).
+# Reconstructed from upstream sources. `expected_*` are Table-5 fingerprints used
+# for validation. `intended_*` record the paper's offline downsampling (we ingest
+# the FULL upstream per user decision, recording the paper's intended subset here).
+#
+# source: lotsa | chronos | chronos_extra | tempopfn
+# location:
+#   lotsa         -> subdir in Salesforce/lotsa_data
+#   chronos       -> config name in autogluon/chronos_datasets
+#                    (KernelSynth: training_corpus/kernel_synth_1m parquet glob)
+#   chronos_extra -> config name in autogluon/chronos_datasets_extra (Kaggle-backed)
+#   tempopfn      -> generator wrapper key in automl/TempoPFN
+
+datasets:
+  # ---------------- LOTSA (Salesforce/lotsa_data) ----------------
+  - {name: bdg2_bull,            paper_name: "BDG-2 Bull",            source: lotsa, location: bull,                       domain: Energy,   freq: H,   expected_series: 41,      num_variates: 1,  expected_max_length: 12280,  weight: 25}
+  - {name: bdg2_fox,             paper_name: "BDG-2 Fox",             source: lotsa, location: bdg-2_fox,                 domain: Energy,   freq: H,   expected_series: 135,     num_variates: 1,  expected_max_length: 12280,  weight: 5}
+  - {name: bdg2_panther,         paper_name: "BDG-2 Panther",         source: lotsa, location: bdg-2_panther,             domain: Energy,   freq: H,   expected_series: 105,     num_variates: 1,  expected_max_length: 6132,   weight: 2.5}
+  - {name: buildings_900k,       paper_name: "BuildingsBench900k",    source: lotsa, location: buildings_900k,            domain: Energy,   freq: H,   expected_series: 100000,  num_variates: 1,  expected_max_length: 8759,   weight: 0.02048, subsample_series: 100000, note: "paper subsamples 100k random series (raw ~1.8M)"}
+  - {name: residential_load_power, paper_name: "Residential Load Power", source: lotsa, location: residential_load_power, domain: Energy, freq: 1T, expected_series: 271,   num_variates: 3,  expected_max_length: 614880, weight: 1.2}
+  - {name: residential_pv_power, paper_name: "Residential PV Power",  source: lotsa, location: residential_pv_power,      domain: Energy,   freq: 1T,  expected_series: 233,     num_variates: 3,  expected_max_length: 614880, weight: 1.5}
+  - {name: china_air_quality,    paper_name: "China Air Quality",     source: lotsa, location: china_air_quality,         domain: Climate,  freq: H,   expected_series: 437,     num_variates: 6,  expected_max_length: 397335, weight: 0.3}
+  - {name: cmip6_2000,           paper_name: "CMIP6 2000",            source: lotsa, location: cmip6_2000,               domain: Climate,  freq: 6H,  expected_series: 8192,    num_variates: 22, expected_max_length: 7300,   weight: 0.057, subsample_channels: 22, note: "paper subsamples 22 of 53 channels"}
+  - {name: era5_1989,            paper_name: "ERA5 1989",             source: lotsa, location: era5_1989,                domain: Climate,  freq: H,   expected_series: 8192,    num_variates: 15, expected_max_length: 8736,   weight: 0.085, subsample_channels: 15, note: "paper subsamples 15 of 45 channels"}
+  - {name: era5_1990,            paper_name: "ERA5 1990",             source: lotsa, location: era5_1990,                domain: Climate,  freq: H,   expected_series: 8192,    num_variates: 15, expected_max_length: 8736,   weight: 0.085, subsample_channels: 15, note: "paper subsamples 15 of 45 channels"}
+  - {name: era5_1991,            paper_name: "ERA5 1991",             source: lotsa, location: era5_1991,                domain: Climate,  freq: H,   expected_series: 8192,    num_variates: 15, expected_max_length: 8736,   weight: 0.085, subsample_channels: 15, note: "paper subsamples 15 of 45 channels"}
+  - {name: subseasonal,          paper_name: "Subseasonal",           source: lotsa, location: subseasonal,              domain: Climate,  freq: 1D,  expected_series: 862,     num_variates: 4,  expected_max_length: 16470,  weight: 0.3}
+  - {name: subseasonal_precip,   paper_name: "Subseasonal Precipitation", source: lotsa, location: subseasonal_precip,   domain: Climate,  freq: 1D,  expected_series: 862,     num_variates: 1,  expected_max_length: 11323,  weight: 1.2}
+  - {name: pems04,               paper_name: "PEMS04",                source: lotsa, location: PEMS04,                   domain: Traffic,  freq: 5T,  expected_series: 307,     num_variates: 3,  expected_max_length: 16992,  weight: 1.2}
+  - {name: pems07,               paper_name: "PEMS07",                source: lotsa, location: PEMS07,                   domain: Traffic,  freq: 5T,  expected_series: 883,     num_variates: 1,  expected_max_length: 28224,  weight: 1.2}
+  - {name: pems08,               paper_name: "PEMS08",                source: lotsa, location: PEMS08,                   domain: Traffic,  freq: 5T,  expected_series: 170,     num_variates: 3,  expected_max_length: 17856,  weight: 2.1}
+  - {name: q_traffic,            paper_name: "Q-TRAFFIC",             source: lotsa, location: Q-TRAFFIC,                domain: Traffic,  freq: 15T, expected_series: 45148,   num_variates: 1,  expected_max_length: 5856,   weight: 0.024}
+  - {name: alibaba_cluster_trace_2018, paper_name: "Alibaba Cluster Trace 2018", source: lotsa, location: alibaba_cluster_trace_2018, domain: Cloud, freq: 5T, expected_series: 58409, num_variates: 2, expected_max_length: 1728, weight: 0.009}
+  - {name: monash_m3_monthly,    paper_name: "Monash M3 Monthly",     source: lotsa, location: monash_m3_monthly,         domain: Econ/Fin, freq: M,   expected_series: 1428,    num_variates: 1,  expected_max_length: 126,    weight: 0.72}
+  - {name: nn5_weekly,           paper_name: "NN5 Weekly",            source: lotsa, location: nn5_weekly,               domain: Econ/Fin, freq: W,   expected_series: 111,     num_variates: 1,  expected_max_length: 105,    weight: 5}
+  - {name: project_tycho,        paper_name: "Project Tycho",         source: lotsa, location: project_tycho,            domain: Health,   freq: W,   expected_series: 1258,    num_variates: 1,  expected_max_length: 3854,   weight: 0.21}
+
+  # ---------------- Chronos (autogluon/chronos_datasets) ----------------
+  - {name: australian_electricity, paper_name: "Australian Electricity", source: chronos, location: monash_australian_electricity, domain: Energy, freq: 30T, expected_series: 5, num_variates: 1, expected_max_length: 232272, weight: 220}
+  - {name: wind_farms_hourly,    paper_name: "Wind Farms H",          source: chronos, location: wind_farms_hourly,         domain: Energy,   freq: H,   expected_series: 337,     num_variates: 1,  expected_max_length: 6148,   weight: 4}
+  - {name: wind_farms_daily,     paper_name: "Wind Farms D",          source: chronos, location: wind_farms_daily,          domain: Energy,   freq: D,   expected_series: 337,     num_variates: 1,  expected_max_length: 366,    weight: 2}
+  - {name: weatherbench_daily,   paper_name: "Weatherbench daily",    source: chronos, location: weatherbench_daily,        domain: Climate,  freq: 1D,  expected_series: 10000,   num_variates: 1,  expected_max_length: 14610,  weight: 0.1024, subsample_series: 10000, note: "paper subsamples 10k random series"}
+  - {name: mexico_city_bikes,    paper_name: "Mexico City Bikes",     source: chronos, location: mexico_city_bikes,         domain: Traffic,  freq: H,   expected_series: 494,     num_variates: 1,  expected_max_length: 104449, weight: 2.5}
+  - {name: taxi_30min,           paper_name: "Taxi (30 Min.)",        source: chronos, location: taxi_30min,                domain: Traffic,  freq: 30T, expected_series: 2428,    num_variates: 1,  expected_max_length: 1488,   weight: 0.88}
+  - {name: taxi_1h,              paper_name: "Taxi (Hourly)",         source: chronos, location: taxi_1h,                   domain: Traffic,  freq: H,   expected_series: 2428,    num_variates: 1,  expected_max_length: 744,    weight: 0.88}
+  - {name: uber_tlc_hourly,      paper_name: "Uber TLC (Hourly)",     source: chronos, location: uber_tlc_hourly,           domain: Traffic,  freq: H,   expected_series: 262,     num_variates: 1,  expected_max_length: 4344,   weight: 4}
+  - {name: wiki_daily_100k,      paper_name: "Wiki Daily",            source: chronos, location: wiki_daily_100k,           domain: Web,      freq: D,   expected_series: 100000,  num_variates: 1,  expected_max_length: 2741,   weight: 0.00512}
+  - {name: kernel_synth_1m,      paper_name: "Kernel Synth 1M",       source: chronos, location: "training_corpus/kernel_synth_1m", domain: Synthetic, freq: null, expected_series: 1000000, num_variates: 1, expected_max_length: 1024, weight: 0.001024}
+
+  # ---------------- Kaggle via chronos_datasets_extra (needs Kaggle auth) ----------------
+  - {name: spanish_weather,      paper_name: "Spanish Weather",       source: chronos_extra, location: spanish_energy_and_weather, domain: Climate, freq: H, expected_series: 5, num_variates: 3, expected_max_length: 24544, weight: 105, needs_kaggle: true, note: "5 cities x 3 weather channels; pick temp/pressure/humidity"}
+
+  # ---------------- Synthetic (automl/TempoPFN generators, regenerated/seeded) ----------------
+  - {name: syn_anomaly,     paper_name: "Anomaly",     source: tempopfn, location: AnomalyGenerator,                domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 10000, weight: 0.0256}
+  - {name: syn_forecastpfn, paper_name: "ForecastPFN", source: tempopfn, location: ForecastPFNGenerator,            domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 10000, weight: 1}
+  - {name: syn_gp,          paper_name: "GP",          source: tempopfn, location: GPGenerator,                     domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 2048, weight: 0.4096, note: "DEVIATION: length 2048 instead of paper's 10000. Exact GP at length 10000 is computationally infeasible (~24h; Cholesky fails -> symeig on 10k x 10k matrices, minutes each). 2048 is a standard GP/KernelSynth prior length and keeps full 5000 series."}
+  - {name: syn_sawtooth,    paper_name: "Sawtooth",    source: tempopfn, location: SawToothGenerator,               domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 10000, weight: 0.0512}
+  - {name: syn_sinewave,    paper_name: "Sinewave",    source: tempopfn, location: SineWaveGenerator,               domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 10000, weight: 0.1024}
+  - {name: syn_spikes,      paper_name: "Spikes",      source: tempopfn, location: SpikesGenerator,                 domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 10000, weight: 0.0256}
+  - {name: syn_step,        paper_name: "Step",        source: tempopfn, location: StepGenerator,                   domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 10000, weight: 0.0512}
+  - {name: syn_ou,          paper_name: "OU",          source: tempopfn, location: OrnsteinUhlenbeckProcessGenerator, domain: Synthetic, freq: null, expected_series: 5000, num_variates: 1, expected_max_length: 10000, weight: 0.4096}

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meta/reports/alibaba_cluster_trace_2018.json

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meta/reports/australian_electricity.json

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meta/reports/bdg2_bull.json

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meta/reports/bdg2_fox.json

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meta/reports/bdg2_panther.json

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meta/reports/buildings_900k.json

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meta/reports/china_air_quality.json

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meta/reports/cmip6_2000.json

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meta/reports/era5_1989.json

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meta/reports/era5_1990.json

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meta/reports/era5_1991.json

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meta/reports/kernel_synth_1m.json

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meta/reports/mexico_city_bikes.json

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meta/reports/monash_m3_monthly.json

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meta/reports/nn5_weekly.json

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meta/reports/pems04.json

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meta/reports/pems07.json

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meta/reports/pems08.json

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meta/reports/project_tycho.json

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meta/reports/q_traffic.json

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meta/reports/residential_load_power.json

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meta/reports/residential_pv_power.json

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meta/reports/subseasonal.json

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meta/reports/subseasonal_precip.json

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meta/reports/syn_anomaly.json

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meta/reports/syn_forecastpfn.json

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meta/reports/syn_gp.json

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meta/reports/syn_ou.json

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meta/reports/syn_sawtooth.json

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meta/reports/syn_sinewave.json

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+}

meta/reports/syn_spikes.json

@@ -0,0 +1,39 @@
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+}

meta/reports/syn_step.json

@@ -0,0 +1,39 @@
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+}

meta/reports/taxi_1h.json

@@ -0,0 +1,39 @@
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meta/reports/taxi_30min.json

@@ -0,0 +1,39 @@
+{
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meta/reports/uber_tlc_hourly.json

@@ -0,0 +1,39 @@
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meta/reports/weatherbench_daily.json

@@ -0,0 +1,39 @@
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meta/reports/wiki_daily_100k.json

@@ -0,0 +1,43 @@
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meta/reports/wind_farms_daily.json

@@ -0,0 +1,39 @@
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meta/reports/wind_farms_hourly.json

@@ -0,0 +1,39 @@
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