Contrastive learning enhanced by graph neural networks for Universal Multivariate Time Series Representation

Abstract

Analyzing multivariate time series data is crucial for many real-world issues, such as power forecasting, traffic flow forecasting, industrial anomaly detection, and more. Recently, universal frameworks for time series representation based on representation learning have received widespread attention due to their ability to capture changes in the distribution of time series data. However, existing time series representation learning models, when confronting multivariate time series data, merely apply contrastive learning methods to construct positive and negative samples for each variable at the timestamp level, and then employ a contrastive loss function to encourage the model to learn the similarities among the positive samples and the dissimilarities among the negative samples for each variable. Despite this, they fail to fully exploit the latent space dependencies between pairs of variables. To address this problem, we propose the Contrastive Learning Enhanced by Graph Neural Networks for Universal Multivariate Time Series Representation (COGNet), which has three distinctive features. (1) COGNet is a comprehensive self-supervised learning model that combines autoencoders and contrastive learning methods. (2) We introduce graph feature representation blocks on top of the backbone encoder, which extract adjacency features of each variable with other variables. (3) COGNet uses graph contrastive loss to learn graph feature representations. Experimental results across multiple public datasets indicate that COGNet outperforms existing methods in time series prediction and anomaly detection tasks.