CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

TL;DR

This paper introduces CoST, a contrastive learning framework that learns disentangled seasonal and trend representations for time series forecasting, significantly improving accuracy over existing methods.

Contribution

It proposes a novel contrastive learning approach for disentangled seasonal-trend representations, enhancing forecasting performance and robustness.

Findings

Achieves 21.3% improvement in MSE on multivariate datasets

Outperforms state-of-the-art methods consistently

Robust across different backbone encoders and regressors

Abstract

Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective. Following this principle, we propose a new time series representation learning framework for time series forecasting named CoST, which applies contrastive learning methods to learn disentangled seasonal-trend representations. CoST comprises both time domain and frequency domain contrastive losses to learn discriminative trend and seasonal representations, respectively. Extensive experiments on real-world datasets show that CoST consistently outperforms the state-of-the-art methods by a considerable margin, achieving a 21.3% improvement in MSE on multivariate benchmarks. It is also robust to various choices of backbone encoders, as well as downstream regressors. Code is available at https://github.com/salesforce/CoST.