关键词: 股票预测, 互补集成经验模态分解, 长短时记忆网络, 宽度学习系统

Abstract: As a core component of the capital market, the price trends and future trend prediction in the stock market are among the most important concerns for investors. Accurate prediction of stock prices can provide strong technical support for investors. For the past few years, deep learning has been widely used for stock prediction to explore more effective information. However, the nonlinearity, complexity, and multiscale characteristics of stock data make it relatively difficult to extract hidden information, and deep learning prediction models suffer from issues such as vanishing gradients and time delays, making it challenging to fit when the sequence exhibits significant fluctuations. Thus, there is an urgent need to improve prediction accuracy.
Long Short-Term Memory (LSTM) networks have been widely applied to stock prediction in recent years, but their structural characteristics can easily fall into local optima, thereby affecting prediction accuracy. Drawing on the good approximation ability of the Broad Learning System (BLS) in time series forecasting, this study attempts to combine width learning with deep learning. It constructs an LSTM-BLS model by utilizing LSTM for feature extraction, feeding the extracted features to the mapping nodes of BLS through fully connected layers, generating enhanced nodes, and calculating the prediction values. Additionally, to address the non-stationary nature of stock sequences, Complementary Ensemble Empirical Mode Decomposition (CEEMD) is introduced for denoising. The adaptive decomposition characteristic of CEEMD does not excessively increase the complexity of the model. Therefore, the proposed CEEMD-LSTM-BLS stock prediction model is presented. The model is implemented using the Keras framework in the Python language, and empirical research is conducted using closing price data of the Agriculture, Forestry, Animal Husbandry, and Fishery Index (399231) from the China Stock Market & Accounting Research Database (CSMAR).
Three evaluation metrics, namely mean absolute error, root mean square error, and coefficient of determination, are selected to assess the performance of the model. By comparing the LSTM-BLS model with baseline models and existing stock prediction models, it is demonstrated that the fusion of Deep Learning and Broad Learning System shows significant improvement in multiple accuracy indicators. Particularly, when comparing the CEEMD-LSTM-BLS model with the CEEMD-LSTM model without BLS integration, it is found that the LSTM model exhibits certain errors in prediction, especially at turning points where the volatility is higher, leading to more pronounced prediction errors. The BLS module in the CEEMD-LSTM-BLS model can address such issues. When the data exhibits significant fluctuations, the proposed new model in this study outperforms existing models in terms of fitting discrepancies and time delays. Therefore, the proposed CEEMD-LSTM-BLS stock closing price prediction model can accurately forecast the market’s ups and downs, providing valuable reference for investors.
However, this study only considers the single factor of closing price, which has certain limitations. Therefore, the future experimental direction will be based on this study and consider multiple-factor input prediction to prevent information compression issues caused by single-factor input for highly volatile data, which can hinder the achievement of the desired prediction accuracy. The aim is to construct a stock prediction model that integrates deep learning and width learning systems under the influence of multiple variables and factors. Finally, the authors express their gratitude to the mentors for their guidance and support throughout this research.

Key words: stock forecasting, Complementary Ensemble Empirical Mode Decomposition, Long Short-Term Memory, Broad Learning System