用拓扑分析预测股市走势，对比多种方法组合效果
Exploring applications of topological data analysis in stock index movement prediction
- 三种点云构建法结合拓扑特征提取
- 15种特征组合在4个指数数据集上测试
- 揭示不同拓扑配置对预测效果的影响
拓扑数据分析（TDA）在金融预测领域受到关注，但点云构造方法、拓扑特征表示和分类模型的选择对结果影响显著。本文研究股票指数走势分类问题：首先采用三种方法构建指数点云；随后应用TDA从点云中提取拓扑结构，计算四种不同的拓扑特征，并枚举15种特征组合输入六种机器学习模型；在CSI、DAX、HSI和FTSE等数据集上进行指数走势分类任务，评估不同TDA配置的预测性能，为各类拓扑设置的有效性提供实证依据。
原文摘要 · Abstract (English)
Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representations, and classification models has a substantial impact on prediction results. This paper addresses the classification problem of stock index movement. First, we construct point clouds for stock indices using three different methods. Next, we apply TDA to extract topological structures from the point clouds. Four distinct topological features are computed to represent the patterns in the data, and 15 combinations of these features are enumerated and input into six different machine learning models. We evaluate the predictive performance of various TDA configurations by conducting index movement classification tasks on datasets such as CSI, DAX, HSI and FTSE providing insights into the efficiency of different TDA setups.
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