用改进的Hidformer模型提升股票价格预测准确率
Hidformer: Transformer-Style Neural Network in Stock Price Forecasting
- 将Transformer思想与技术分析结合，改进Hidformer用于时序预测
- 实验验证该模型在股票价格预测中表现优于传统方法
- 适合对算法交易和金融时序建模感兴趣的读者
本文研究基于Transformer的神经网络在股票价格预测中的应用，重点关注机器学习技术与金融市场分析的交叉。回顾了Transformer模型从起源到金融时序分析应用的演进过程。核心是探讨当前在时序预测中表现优异的Hidformer模型是否适用于股票价格预测。本文采用经过微调的Hidformer模型，融合技术分析原理与先进机器学习概念，以提升预测精度。通过一系列评估标准检验其性能，结果为Transformer架构在金融时序预测中的实际应用提供了新见解，表明其有潜力优化算法交易策略，包括辅助人类决策。
原文摘要 · Abstract (English)
This paper investigates the application of Transformer-based neural networks to stock price forecasting, with a special focus on the intersection of machine learning techniques and financial market analysis. The evolution of Transformer models, from their inception to their adaptation for time series analysis in financial contexts, is reviewed and discussed. Central to our study is the exploration of the Hidformer model, which is currently recognized for its promising performance in time series prediction. The primary aim of this paper is to determine whether Hidformer will also prove itself in the task of stock price prediction. This slightly modified model serves as the framework for our experiments, integrating the principles of technical analysis with advanced machine learning concepts to enhance stock price prediction accuracy. We conduct an evaluation of the Hidformer model's performance, using a set of criteria to determine its efficacy. Our findings offer additional insights into the practical application of Transformer architectures in financial time series forecasting, highlighting their potential to improve algorithmic trading strategies, including human decision making.
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