Weight-Training Ensemble Model for Stock Price Forecast

Abstract

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.