Masked time series modeling has recently gained much attention as a self-supervised representation learning strategy for time series. Inspired by masked image modeling in computer vision, recent works first patchify and partially mask out time series, and then train Transformers to capture the dependencies between patches by predicting masked patches from unmasked patches. However, we argue that capturing such patch dependencies might not be an optimal strategy for time series representation learning; rather, learning to embed patches independently results in better time series representations. Specifically, we propose to use 1) the simple patch reconstruction task, which autoencode each patch without looking at other patches, and 2) the simple patch-wise MLP that embeds each patch independently. In addition, we introduce complementary contrastive learning to hierarchically capture adjacent time series information efficiently. Our proposed method improves time series forecasting and classification performance compared to state-of-the-art Transformer-based models, while it is more efficient in terms of the number of parameters and training/inference time. Code is available at this repository: https://github.com/seunghan96/pits.

翻译：掩码时间序列建模近期作为时间序列自监督表示学习策略受到了广泛关注。受计算机视觉中掩码图像建模的启发，现有工作首先对时间序列进行补丁划分并部分掩码，随后训练Transformer模型通过未掩码补丁预测掩码补丁来捕捉补丁间的依赖关系。然而，我们认为捕捉此类补丁间依赖关系可能并非时间序列表示学习的最优策略；相反，学习独立嵌入补丁能够获得更好的时间序列表示。具体而言，我们提出采用：1) 简单的补丁重构任务，即对每个补丁进行自编码而不依赖其他补丁；2) 简单的逐补丁MLP，对每个补丁进行独立嵌入。此外，我们引入互补对比学习以高效分层捕捉相邻时间序列信息。与基于Transformer的最先进模型相比，所提方法在时间序列预测和分类性能上均有提升，同时在参数量和训练/推理时间方面更具效率。代码开源地址：https://github.com/seunghan96/pits