用可解释AI提升股票预测准确率，让模型决策更透明
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
- 结合Transformer与XAI技术，增强股价预测可解释性
- 在BIST100银行股数据上，模型预测性能显著优于传统方法
- 适合关注金融决策透明度的投资者与量化研究者
金融素养日益依赖于对复杂数据的解读与先进预测工具的应用。本文提出一种新方法，将基于Transformer的时间序列模型与可解释人工智能（XAI）结合，提升股价预测的准确性和可解释性。研究聚焦于BIST100指数中交易量最高的五家银行股，以及XBANK和XU100指数，时间跨度为2015年1月至2025年3月。采用DLinear、LTSNet、Vanilla Transformer和Time Series Transformer等模型，并引入技术指标作为输入特征。通过SHAP和LIME方法揭示各特征对模型输出的影响，结果表明变压器类模型具备强大的预测能力，且可解释机器学习有助于个体做出更明智的投资决策，积极参与金融市场。
原文摘要 · Abstract (English)
Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 indices, covering the period from January 2015 to March 2025. Models including DLinear, LTSNet, Vanilla Transformer, and Time Series Transformer are employed, with input features enriched by technical indicators. SHAP and LIME techniques are used to provide transparency into the influence of individual features on model outputs. The results demonstrate the strong predictive capabilities of transformer models and highlight the potential of interpretable machine learning to empower individuals in making informed investment decisions and actively engaging in financial markets.
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