1 research outputs found
Gated Res2Net for Multivariate Time Series Analysis
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
superiorit