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.

本研究解决了股票预测任务中缺乏明确固定空间关系的问题，并提出了一种双路径自适应相关空间-时间反向变换器（DPA-STIFormer），通过特征变化建模节点信息。该模型通过双路径方式同时提取不同的空间相关性，实验结果表明其在多个股票市场数据集上表现出色，有力证明了模型在挖掘潜在时间相关模式方面的能力。

双路径自适应相关空间-时间反向变换器用于股票时间序列预测