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 history context. Moreover, the construction of positive and negative pairs in current technologies strongly relies on specific time series characteristics, restricting their generalization across diverse types of time series data. 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 does not rely on negative pairs or specific assumptions about the characteristics of the particular time series. Our extensive experiments on several benchmark time series forecasting datasets show that SimTS achieves competitive performance compared to existing contrastive learning methods. Furthermore, 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.

翻译：对比学习方法在图像或时间序列分类任务中展现出学习有意义表示的卓越能力。然而，这些方法在时间序列预测中效果欠佳，因为实例判别优化无法直接应用于从历史上下文预测未来状态。此外，当前技术中正负样本对的构建高度依赖特定时间序列特征，限制了其在不同类型时间序列数据间的泛化能力。为解决上述局限，我们提出SimTS——一种通过潜在空间中“从过去预测未来”来提升时间序列预测性能的简单表示学习方法。SimTS既不依赖负样本对，也不对特定时间序列特性做出特定假设。在多个基准时间序列预测数据集上的大量实验表明，与现有对比学习方法相比，SimTS取得了具有竞争力的性能。此外，我们通过详尽的消融研究揭示了当前用于时间序列预测的对比学习框架的缺陷。总体而言，我们的工作表明SimTS是时间序列预测中其他对比学习方法的一种有前景的替代方案。