MvTS-library: An open library for deep multivariate time series forecasting

Abstract

Modeling multivariate time series has been a subject for a long time, which attracts the attention of scholars from many fields including economics, finance, traffic, etc. As the number of models increases, it is desired to design a unified framework to implement and evaluate these models. Based on Pytorch, we propose MvTS, an open library for multivariate time series forecasting. Through a highly modular design, MvTS systematically integrates the various stages of the whole process of model training. Currently, the library contains 33 models and 23 datasets, and is available at https://github.com/MTS-BenchMark/MvTS. Based on MvTS, we conduct extensive experiments on public datasets and demonstrate that the models reproduced by MvTS are effective and universally applicable to many other datasets. MvTS is a systematic, comprehensive, extensible, and easy-to-use multivariate time series forecasting library, and we believe it will contribute to the research of multivariate time series to some extent.