Multi-Attention Generative Adversarial Network for Multivariate Time Series Prediction

Abstract

Multivariate Time series data play important roles in our daily life. How to use these data in the process of prediction is a highly attractive study for many researchers. To achieve this goal, in this paper, we present a novel multivariate time series prediction method based on multi-attention generative adversarial network. This method includes three phases to explore multivariate time series prediction. Firstly, the encoder stage consists of two modules, from which the input-attention and self-attention can encode the exogenous sequence into latent space. Secondly, the decoder stage consists of the temporal-convolution-attention module, which can extract long-term temporal patterns. To solve the problem of low accuracy in long-term prediction, inspired by the weight clipping method, we design an improved discrimination network finally. The experiment results indicate that multi-attention mechanism is useful and the discrimination network can improve the performance in multivariate time series prediction. We also tested extensive empirical studies with five real world datasets (NASDAQ100, SML2010, Energy, EEG and Air Quality) demonstrate the effectiveness and robustness of our proposed approach.