Paper Link:MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
Train and evaluate MSGNet
sh ./scripts/ETTh1.sh
.Train your model
./models/your_model.py
.Flight dataset
./dataset
.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.