---
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.