用于时间序列预测的简单对比表示学习
Simple Contrastive Representation Learning for Time Series Forecasting
AI总结：
针对对比学习方法在时间序列预测中效果不佳的问题，提出了一种仅使用正样本对、不依赖负样本对的简单表示学习方法SimTS，通过在潜在空间中从过去预测未来来改进预测效果。
摘要：
对比学习方法在为图像或时间序列分类学习有意义的表示方面展现了出色的能力。然而，这些方法对于时间序列预测的效果较差，因为实例判别的优化并不直接适用于从历史上下文中预测未来状态。为了解决这些局限性，我们提出了SimTS，这是一种简单的表示学习方法，旨在通过在潜在空间中学习从过去预测未来，来改进时间序列预测。SimTS仅使用正样本对，不依赖负样本对或给定时间序列的特定特征。此外，我们通过详细的消融研究，展示了当前用于时间序列预测的对比学习框架的缺点。总体而言，我们的工作表明SimTS是其他用于时间序列预测的对比学习方法的一个有前景的替代方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
