The WEATHER-5K dataset is a large-scale time series forecasting dataset containing weather data from 5,672 weather stations worldwide. It is a valuable resource for researchers and developers in the field of time-series forecasting, providing a comprehensive evaluation of various methods and models. WEATHER-5K dataset consists of 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 (temperature, dewpint temperature, wind speed, wind rate, sea level pressure), providing a more reliable and interpretable resource for forecasting.

Until now, we have bnchmarked the following models in this repo:

• iTransformer - iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [ICLR 2024] [Code].

• Corrformer - nterpretable weather forecasting for worldwide stations with a unified deep model [NMI 2023] [Code].

• PatchTST - A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [ICLR 2023] [Code].

• DLinear - Are Transformers Effective for Time Series Forecasting? [AAAI 2023] [Code].

• FEDformer - FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [ICML 2022] [Code].

• Pyraformer - Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting [ICLR 2022] [Code].

• Autoformer - Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [NeurIPS 2021] [Code].

• Informer - Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [AAAI 2021] [Code].

The results are reported at 4 different prediction lengths: 24, 72, 120, and 168, where the input length is 48.

1. Install Python 3.8. For convenience, execute the following command.

pip install -r requirements.txt

2. Prepare Data. You can obtain the well pre-processed datasets from [OneDrive], Then place and unzip the downloaded data in the folder./WEATHER-5K.

3. Train and evaluate model. We provide the experiment scripts for all benchmarks under the folder ./scripts/. You can reproduce the experiment results as the following examples:

# Global Station Weather Forecasting
bash ./scripts/weather-5k/iTransformer.sh

4. Develop your own model.

• Add the model file to the folder ./models. You can follow the ./models/Transformer.py.
• Include the newly added model in the Exp_Basic.model_dict of ./exp/exp_basic.py.
• Create the corresponding scripts under the folder ./scripts.

This library is constructed based on the Time-Series-Library. We sincerely thank the contributors for their contributions.