Simple Contrastive Representation Learning for Time Series Forecasting
Abstract
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or 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 state 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 current contrastive learning framework 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.
Cite
@article{arxiv.2303.18205,
title = {Simple Contrastive Representation Learning for Time Series Forecasting},
author = {Xiaochen Zheng and Xingyu Chen and Manuel Schürch and Amina Mollaysa and Ahmed Allam and Michael Krauthammer},
journal= {arXiv preprint arXiv:2303.18205},
year = {2024}
}
Comments
Extended version. A shortened version was accepted by the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), see https://ieeexplore.ieee.org/document/10446875