A contrastive learning based universal representation for time series forecasting

Abstract

Time series forecasting has wide applications in our daily lives, such as meteorological warnings and decision-making. However, traditional supervised models do not perform well on forecasting tasks due to the lack of annotated training data available in real time series. Recently, researchers have proposed self-supervised methods, especially the contrastive learning approach, which can build a universal representation framework for downstream tasks through data augmentation and contrastive loss. Although it alleviates the shortage of labeled data, the data augmentation approach directly transferred from computer vision is not appropriate for the time domain due to noise vectors and unrelated variables that may interfere with the accuracy of representation. In this paper, we propose a novel time series forecasting model based on disentangled seasonal-trend representation named ACST. It employs an improved cycle generative adversarial data augmentation method to generate samples close to real data for contrastive loss. Moreover, we apply gated residual networks and a noise decomposition module to reduce the impact of different noise vectors and feature variable weights on the results. Extensive experiments show that ACST achieves an average improvement of 26.8% on six benchmarks.