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Workshop: 4th Workshop on Self-Supervised Learning: Theory and Practice

Simple Contrastive Representation Learning for Time Series Forecasting

Xiaochen Zheng · Xingyu Chen · Manuel Schürch · Maolaaisha Aminanmu · Ahmed Allam · Michael Krauthammer


Contrastive learning methods have shown an impressive ability to learn meaningful representations for image and time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discrimination is not directly applicable to predicting the future outcomes from the historical context. To address these limitations, we propose SimTS, a simple representation learning approach for improving time series forecasting by learning to predict the future from the past in the latent space. SimTS exclusively uses positive pairs and does not depend on negative pairs or specific characteristics of a given time series. In addition, we show the shortcomings of the common contrastive learning frameworks used for time series forecasting through a detailed ablation study. Overall, our work suggests that SimTS is a promising alternative to other contrastive learning approaches for time series forecasting.

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