- Install Python 3.8. For convenience, execute the following command.
pip install -r requirements.txt
-
Prepare Data. You can obtained the well pre-processed datasets from [Google Drive], [Tsinghua Cloud] or [Baidu Drive]. Then place the downloaded data under the folder
./dataset. -
Train and evaluate model. We provide the experiment scripts of all benchmarks under the folder
./scripts/. You can reproduce the experiment results as the following examples:
bash ./scripts/long_term_forecast/ETT_script/SCNN_ETTh1.sh
If you find this repo useful, please cite our paper.
@ARTICLE{10457027,
author={Deng, Jinliang and Chen, Xiusi and Jiang, Renhe and Du Yin and Yang, Yi and Song, Xuan and Tsang, Ivor W.},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting},
year={2024},
volume={36},
number={8},
pages={3783-3800},
keywords={Time series analysis;Forecasting;Adaptation models;Convolution;Predictive models;Deep learning;Transformers;Deep learning;disentanglement;spatial-temporal data mining;time series forecasting},
doi={10.1109/TKDE.2024.3371931}}
If you have any questions or suggestions, feel free to contact:
- Jinliang Deng (jinliangdeng9588@gmail.com)
or describe it in Issues.
This repo is constructed based on the following library:
- Time-Series-Library: https://github.com/thuml/Time-Series-Library
All the experiment datasets are public and we obtain them from the following links:
- Long-term Forecasting and Imputation: https://github.com/thuml/Autoformer