用双路径机制捕捉股票时序数据中的动态空间关系，提升预测性能。
Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting
- 以特征变化为令牌，双路径并行提取时间与特征空间相关性
- 在4个股市数据集上达到领先效果，显著捕捉隐含时序关联模式
- 适合关注金融时序建模与动态关系挖掘的研究者
时空图神经网络（STGNN）在多种时序预测任务中取得显著成果。然而，由于股票预测任务缺乏明确且固定的时空关系，许多STGNN在此领域表现不佳。尽管部分STGNN尝试从时序数据中学习空间关系，但往往不够全面。研究表明，使用特征变化作为令牌建模时序序列，可揭示与使用时间步作为令牌完全不同的信息。为更全面地从股票数据中提取动态空间信息，本文提出双路径自适应相关时空反向变换器（DPA-STIFormer）。该模型将每个节点通过连续特征变化作为令牌进行建模，并引入双方向自适应融合机制，将节点编码分解为时间与特征表示，通过双路径方法同时提取不同空间相关性，提出双路径门控机制融合两类相关性信息。在四个股票市场数据集上的实验表明，该模型达到当前最优性能，验证了其在挖掘潜在时序相关模式方面的优越能力。
原文摘要 · Abstract (English)
Spatial-temporal graph neural networks (STGNNs) have achieved significant success in various time series forecasting tasks. However, due to the lack of explicit and fixed spatial relationships in stock prediction tasks, many STGNNs fail to perform effectively in this domain. While some STGNNs learn spatial relationships from time series, they often lack comprehensiveness. Research indicates that modeling time series using feature changes as tokens reveals entirely different information compared to using time steps as tokens. To more comprehensively extract dynamic spatial information from stock data, we propose a Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer (DPA-STIFormer). DPA-STIFormer models each node via continuous changes in features as tokens and introduces a Double Direction Self-adaptation Fusion mechanism. This mechanism decomposes node encoding into temporal and feature representations, simultaneously extracting different spatial correlations from a double path approach, and proposes a Double-path gating mechanism to fuse these two types of correlation information. Experiments conducted on four stock market datasets demonstrate state-of-the-art results, validating the model's superior capability in uncovering latent temporal-correlation patterns.
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