Stock Market Index Prediction Using CEEMDAN‐LSTM‐BPNN‐Decomposition Ensemble Model

John Kamwele Mutinda · Abebe Geletu

25 citations0/0 files storedJournal of Applied Mathematics

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

This study investigates the forecasting of the Deutscher Aktienindex (DAX) market index by addressing the nonlinear and nonstationary nature of financial time series data using the CEEMDAN decomposition method. The CEEMDAN technique is used to decompose the time series into intrinsic mode functions (IMFs) and residuals, which are classified into low‐frequency (LF), medium‐frequency (MF), and high‐frequency (HF) components. Long short‐term memory (LSTM) networks are applied to the MF and HF components, while the backpropagation neural network (BPNN) is utilized for the LF components, resulting in a robust hybrid model termed CEEMDAN‐LSTM‐BPNN. To evaluate the performance of the proposed model, we compare it against several benchmark models, including ARIMA, RNN, LSTM, GRU, BIGRU, BILSTM, BPNN, CEEMDAN‐LSTM, CEEMDAN‐GRU, CEEMDAN‐BPNN, and CEEMDAN‐GRU‐BPNN, across different training–testing splits (70% training/30% testing, 80% training/20% testing, and 90% training/10% testing). The model’s predictive accuracy is measured using six metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), root mean squared logarithmic error (RMSLE), and R ‐squared. To further assess model performance, we conduct the Diebold–Mariano (DM) test to compare forecast accuracy between the proposed and benchmark models and the model confidence set (MCS) test to evaluate the statistical significance of the improvement. The results demonstrate that the CEEMDAN‐LSTM‐BPNN model significantly outperforms other methods in terms of accuracy, with the DM and MCS tests confirming the superiority of the proposed model across multiple evaluation metrics. The findings highlight the importance of combining advanced decomposition methods and deep learning models for financial forecasting. This research contributes to the development of more accurate forecasting techniques, offering valuable implications for financial decision‐making and risk management.

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2019The Performance of LSTM and BiLSTM in Forecasting Time Series 2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2020Stock Closing Price Prediction using Machine Learning Techniques 2020A CNN-LSTM-Based Model to Forecast Stock Prices 2021Machine learning in the Chinese stock market 2011Forecasting stock indices with back propagation neural network

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