Gated Res2Net for Multivariate Time Series Analysis

TL;DR

This paper introduces Gated Res2Net, a novel neural network backbone that enhances multivariate time series analysis by effectively capturing temporal information and feature map correlations through a gating mechanism.

Contribution

The paper proposes Gated Res2Net, integrating gating mechanisms into Res2Net to better model feature correlations and control information flow in multivariate time series analysis.

Findings

GRes2Net outperforms state-of-the-art methods on multiple datasets.

The gating mechanism improves feature correlation modeling.

Enhanced temporal and correlation capturing capabilities.

Abstract

Multivariate time series analysis is an important problem in data mining because of its widespread applications. With the increase of time series data available for training, implementing deep neural networks in the field of time series analysis is becoming common. Res2Net, a recently proposed backbone, can further improve the state-of-the-art networks as it improves the multi-scale representation ability through connecting different groups of filters. However, Res2Net ignores the correlations of the feature maps and lacks the control on the information interaction process. To address that problem, in this paper, we propose a backbone convolutional neural network based on the thought of gated mechanism and Res2Net, namely Gated Res2Net (GRes2Net), for multivariate time series analysis. The hierarchical residual-like connections are influenced by gates whose values are calculated based on the original feature maps, the previous output feature maps and the next input feature maps thus considering the correlations between the feature maps more effectively. Through the utilization of gated mechanism, the network can control the process of information sending hence can better capture and utilize the both the temporal information and the correlations between the feature maps. We evaluate the GRes2Net on four multivariate time series datasets including two classification datasets and two forecasting datasets. The results demonstrate that GRes2Net have better performances over the state-of-the-art methods thus indicating the superiority