股票价格预测的权重训练集合模型

Jianing Zhao, Ayana Takai, E. Kita
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摘要

本文采用集合模型对股票价格进行预测。所提出的集成模型是基于对基本算法预测值的加权平均估计。基本算法包括线性回归、长短期记忆(LSTM)、支持向量回归(SVR)和lightGBM。所提模型的性能取决于权重参数。收集过去的数据,计算出集合模型基本模型的权重参数。以丰田汽车公司的股价预测为数值实例。然后构建LSTM、SVR和LightGBM来识别权值序列数据的变化趋势,预测最适合集成的组合权值。实验结果表明,任意集成模型的精度都明显优于单个组件模型。与简单平均和基于误差的组合方法相比,该模型的误差最小。在选择相关组合权值时,即使是很小的差异也会在预测模型的线性组合中起关键作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weight-Training Ensemble Model for Stock Price Forecast
The ensemble model is applied for the stock price prediction in this study. The proposed ensemble model is based on the weighted average estimation of the values predicted by base algorithms. The base algorithms include Linear Regression, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and lightGBM. The performance of the proposed model depends on the weight parameters. The past data are collected to calculate the weigh parameters for base models of the ensemble models. The stock price prediction of Toyota Motor Corporation is considered as the numerical examples. Then LSTM, SVR and LightGBM are built to recognize the trend of the weight sequence data and to predict the most suitable combination weights for ensemble. The experimental results show that any ensemble models achieves significantly better accuracy than each component model. The proposed model also achieved the lowest error than simple average and error-based combination method. Even a tiny difference in choosing associated combining weights can play a crucial role in linear combination of models for prediction.
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