Time Series Forecasting has made significant progress with the help of Patching technique, which partitions time series into multiple patches to effectively retain contextual semantic information into a representation space beneficial for modeling long-term dependencies. However, conventional patching partitions a time series into adjacent patches, which causes a fixed representation space, thus resulting in insufficiently expressful representations. In this paper, we pioneer the exploration of constructing a selective representation space to flexibly include the most informative patches for forecasting. Specifically, we propose the Selective Representation Space (SRS) module, which utilizes the learnable Selective Patching and Dynamic Reassembly techniques to adaptively select and shuffle the patches from the contextual time series, aiming at fully exploiting the information of contextual time series to enhance the forecasting performance of patch-based models. To demonstrate the effectiveness of SRS module, we propose a simple yet effective SRSNet consisting of SRS and an MLP head, which achieves state-of-the-art performance on real-world datasets from multiple domains. Furthermore, as a novel plug-and-play module, SRS can also enhance the performance of existing patch-based models. The resources are available at https://github.com/decisionintelligence/SRSNet.

翻译：时间序列预测在分片技术的帮助下取得了显著进展，该技术将时间序列划分为多个分片，从而有效地将上下文语义信息保留到有利于建模长期依赖关系的表示空间中。然而，传统的分片方法将时间序列划分为相邻的分片，这导致了固定的表示空间，从而导致表示表达能力不足。本文率先探索构建选择性表示空间，以灵活地纳入最具信息量的分片进行预测。具体而言，我们提出了选择性表示空间模块，该模块利用可学习的选择性分片和动态重组技术，自适应地从上下文时间序列中选择和重排分片，旨在充分利用上下文时间序列的信息，以增强基于分片的模型的预测性能。为了证明SRS模块的有效性，我们提出了一个简单而有效的SRSNet，由SRS和一个MLP头部组成，该模型在多个领域的真实世界数据集上实现了最先进的性能。此外，作为一种新颖的即插即用模块，SRS还可以提升现有基于分片的模型的性能。相关资源可在https://github.com/decisionintelligence/SRSNet获取。