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abdulfatirshchur
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Add badges and bibtex to readme (#59)
Co-authored-by: Oleksandr Shchur <[email protected]>
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README.md

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<div align="center">
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# fev
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A lightweight library that makes it easy to benchmark time series forecasting models.
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[![preprint](https://img.shields.io/static/v1?label=Paper&message=2509.26468&color=B31B1B&logo=arXiv)](https://arxiv.org/abs/2509.26468)
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[![fev-bench](https://img.shields.io/badge/%F0%9F%8F%86%20fev--bench-Leaderboard-0078D4)](https://huggingface.co/spaces/autogluon/fev-leaderboard)
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[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20HF-Chronos_Datasets-FFD21E)](https://huggingface.co/datasets/autogluon/chronos_datasets)
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[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20HF-fev_Datasets-FFD21E)](https://huggingface.co/datasets/autogluon/fev_datasets)
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[![License: Apache-2.0](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
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</div>
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`fev` (Forecast EValuation library) is a lightweight package that makes it easy to benchmark time series forecasting models.
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- Extensible: Easy to define your own forecasting tasks and benchmarks.
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- Reproducible: Ensures that the results obtained by different users are comparable.
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Repositories with datasets in format compatible with `fev`:
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- [`chronos_datasets`](https://huggingface.co/datasets/autogluon/chronos_datasets)
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- [`fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets)
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## Citation
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If you find this package useful for your research, please consider citing the associated paper(s):
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```
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@article{shchur2025fev,
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title={{fev-bench}: A Realistic Benchmark for Time Series Forecasting},
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author={Shchur, Oleksandr and Ansari, Abdul Fatir and Turkmen, Caner and Stella, Lorenzo and Erickson, Nick and Guerron, Pablo and Bohlke-Schneider, Michael and Wang, Yuyang},
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year={2025},
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eprint={2509.26468},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```

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