(ICML 2024) [Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling](https://openreview.net/forum?id=87CYNyCGOo).

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🌟 If you find this resource helpful, please consider to star this repository and cite our research:

@inproceedings{
yu2024revitalizing,
title={Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling},
author={Guoqi Yu and Jing Zou and Xiaowei Hu and Angelica I Aviles-Rivero and Jing Qin and Shujun Wang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=87CYNyCGOo}
}

Get Started

1. Install requirements. pip install -r requirements.txt
2. Download data. You can download the all datasets from datasets. All the datasets are well pre-processed and can be used easily.
3. Train the model. We provide the experiment scripts of all benchmarks under the folder ./scripts. You can replicate the benchmark results of Leddam from the paper on the eight datasets by:

python run_script.py

4. Alternatively, you can use bash commands to individually run scripts in the 'scripts' folder from the command line to obtain results for individual datasets, take Traffic for example, you can use the below command line to obtain the result of input-96-predict-96:

bash scripts/Traffic/traffic_96.sh

You can find the training history and result under 'logs/' folder