- Install Python 3.8. For convenience, execute the following command.
pip install -r requirements.txt
-
Prepare Data. Then place the downloaded data in the folder
./dataset. Here is a summary of supported datasets. -
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:
# long-term forecast
bash ./scripts/long_term_forecast/ETT_script/TimesNet_ETTh1.sh
# short-term forecast
bash ./scripts/short_term_forecast/TimesNet_M4.sh
# imputation
bash ./scripts/imputation/ETT_script/TimesNet_ETTh1.sh
# anomaly detection
bash ./scripts/anomaly_detection/PSM/TimesNet.sh
# classification
bash ./scripts/classification/TimesNet.sh
- 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_dictof./exp/exp_basic.py. - Create the corresponding scripts under the folder
./scripts.
If you find this repo useful, please cite our paper.
@inproceedings{wu2023timesnet,
title={TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis},
author={Haixu Wu and Tengge Hu and Yong Liu and Hang Zhou and Jianmin Wang and Mingsheng Long},
booktitle={International Conference on Learning Representations},
year={2023},
}