Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting

TL;DR

This paper introduces DPA-STIFormer, a novel transformer-based model that captures dynamic spatial-temporal correlations in stock data by using feature changes as tokens and a double-path fusion mechanism, achieving state-of-the-art forecasting accuracy.

Contribution

It proposes a double-path adaptive correlation transformer that models feature change tokens and fuses temporal and feature-based spatial correlations for stock prediction.

Findings

Achieves state-of-the-art results on four stock datasets.

Effectively uncovers latent temporal-correlation patterns.

Demonstrates superior capability over existing models.

Abstract

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.