Paper Link:MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
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Train and evaluate MSGNet
- You can use the following command:
sh ./scripts/ETTh1.sh
.
- You can use the following command:
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Train your model
- Add model file in the folder
./models/your_model.py
. - Add model in the class Exp_Main.
- Add model file in the folder
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Flight dataset
- You can obtain the dataset from Google Drive. Then please place it in the folder
./dataset
.
- You can obtain the dataset from Google Drive. Then please place it in the folder
MSGNet employs several ScaleGraph blocks, each encompassing three pivotal modules: an FFT module for multi-scale data identification, an adaptive graph convolution module for inter-series correlation learning within a time scale, and a multi-head attention module for intra-series correlation learning.
Forecast results with 96 review window and prediction length {96, 192, 336, 720}. The best result is represented in bold, followed by underline.
@article{cai2023msgnet,
title={MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting},
author={Cai, Wanlin and Liang, Yuxuan and Liu, Xianggen and Feng, Jianshuai and Wu, Yuankai},
journal={arXiv preprint arXiv:2401.00423},
year={2023}
}
We appreciate the valuable contributions of the following GitHub.
- LTSF-Linear (https://github.com/cure-lab/LTSF-Linear)
- TimesNet (https://github.com/thuml/TimesNet)
- Time-Series-Library (https://github.com/thuml/Time-Series-Library)
- MTGnn (https://github.com/nnzhan/MTGNN)
- Autoformer (https://github.com/thuml/Autoformer)
- Informer (https://github.com/zhouhaoyi/Informer2020)