用分层结构捕捉股市整体与个股关系，提升股票预测精度
Hierarchical Information-Guided Spatio-Temporal Mamba for Stock Time Series Forecasting
- 分层设计同时建模市场整体趋势与个股动态
- 在三个中国指数数据集上达到当前最优性能
- 融合宏观信息优化序列选择，适合量化交易研究者
Mamba 在时间序列预测中表现优异，因其卓越的序列选择机制。然而，传统基于 Mamba 的模型在股票时间序列预测中面临挑战，难以准确捕捉市场整体动态与个股间复杂关联。为此，我们提出分层信息引导时空 Mamba（HIGSTM）框架。HIGSTM 引入索引引导频率滤波分解，从时序中提取共性与个性特征。模型采用精心设计的分层架构，系统捕捉时间动态模式与股市全局静态关系。此外，提出信息引导 Mamba，将宏观信息融入序列选择过程，实现更贴近市场的决策。在 CSI500、CSI800 与 CSI1000 数据集上的全面实验表明，HIGSTM 实现了当前最优性能。
原文摘要 · Abstract (English)
Mamba has demonstrated excellent performance in various time series forecasting tasks due to its superior selection mechanism. Nevertheless, conventional Mamba-based models encounter significant challenges in accurately predicting stock time series, as they fail to adequately capture both the overarching market dynamics and the intricate interdependencies among individual stocks. To overcome these constraints, we introduce the Hierarchical Information-Guided Spatio-Temporal Mamba (HIGSTM) framework. HIGSTM introduces Index-Guided Frequency Filtering Decomposition to extract commonality and specificity from time series. The model architecture features a meticulously designed hierarchical framework that systematically captures both temporal dynamic patterns and global static relationships within the stock market. Furthermore, we propose an Information-Guided Mamba that integrates macro informations into the sequence selection process, thereby facilitating more market-conscious decision-making. Comprehensive experimental evaluations conducted on the CSI500, CSI800 and CSI1000 datasets demonstrate that HIGSTM achieves state-of-the-art performance.
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