WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark
<|reference_start|>WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark: Global Station Weather Forecasting (GSWF) is crucial for various sectors, including aviation, agriculture, energy, and disaster preparedness. Recent advancements in deep learning have significantly improved the accuracy of weather predictions by optimizing models based on public meteorological data. However, existing public datasets for GSWF optimization and benchmarking still suffer from significant limitations, such as small sizes, limited temporal coverage, and a lack of comprehensive variables. These shortcomings prevent them from effectively reflecting the benchmarks of current forecasting methods and fail to support the real needs of operational weather forecasting. To address these challenges, we present the WEATHER-5K dataset. This dataset comprises a comprehensive collection of data from 5,672 weather stations worldwide, spanning a 10-year period with one-hour intervals. It includes multiple crucial weather elements, providing a more reliable and interpretable resource for forecasting. Furthermore, our WEATHER-5K dataset can serve as a benchmark for comprehensively evaluating existing well-known forecasting models, extending beyond GSWF methods to support future time-series research challenges and opportunities. The dataset and benchmark implementation are publicly available at: https://github.com/taohan10200/WEATHER-5K.<|reference_end|>
@article{han2024how,
    title={How far are today's time-series models from real-world weather
  forecasting applications?},
    author={Tao Han, Song Guo, Zhenghao Chen, Wanghan Xu, and Lei Bai},
    journal={arXiv preprint arXiv:2406.14399},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14399},
    primaryClass={cs.LG cs.CV physics.ao-ph stat.ML}
}