Time Series Prediction Based on Temporal Convolutional Network

Abstract

With the development of social life, prediction becomes more and more important. As an emerging sequence modeling model, the temporal convolutional network has been proven to outperform on tasks such as audio synthesis and natural language processing. But it is rarely used for time series prediction. In this paper, we apply the temporal convolutional network into the time series prediction problem. Gated linear units allow the gradient to propagate through the linear unit without scaling so we introduce it in temporal convolutional networks. In order to extract more useful features, we propose a multi-channel gated temporal convolution network model. We use the model for stock closing price prediction, Mackey-Glass time series data prediction, PM2.5 prediction, and appliances energy prediction. The experimental results show that compared with the traditional methods, LSTM, and GRU, the temporal convolutional network, gated temporal convolutional network and multi-channel gated temporal convolution network converge faster and have better performance.