Time series classification based on convolutional network with a Gated Linear Units kernel

Time series data are ubiquitous in human society and nature, and classification is one of the most significant problems in the field of time series mining. Although it has been intensively studied, and has achieved significant results and successful applications, it is still a challenging problem, which requires capturing of multi-scale features of one-dimensional or multi-dimensional time series in variable length. In this paper, we propose a novel time series feature extraction block named Convolutional Gated Linear Units (CGLU), which is a combination of convolutional operations and Gated Linear Units for adaptively extracting local temporal features of time series. Combined with a temporal maxpooling block, it can extract global temporal features. To capture more diverse features, the Inception architecture is adopted to organize the CGLUs with different convolution kernel sizes, which result in the Convolutional GLU network. In order to evaluate the performance, we conduct extensive experiments on the UCR time series datasets (one-dimension) and UEA datasets (multi-dimension). Compared with baselines, our model obtains best results in terms of classification accuracy and training speed, which demonstrate effectiveness and efficiency of CGLUs and Conv-GLU network on time series classification tasks.