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

• URL: http://arxiv.org/abs/2202.01575v1
• Date: Thu, 3 Feb 2022 13:17:38 GMT
• Title: CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
• Authors: Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
• Abstract summary: We propose a new time series representation learning framework named CoST. CoST applies contrastive learning methods to learn disentangled seasonal-trend representations. Experiments on real-world datasets show that CoST consistently outperforms the state-of-the-art methods.
• Score: 35.76867542099019
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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