CEGH: A Hybrid Model Using CEEMD, Entropy, GRU, and History Attention for Intraday Stock Market Forecasting

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 1; p. 71
• Main Authors: Liu, Yijiao, Liu, Xinghua, Zhang, Yuxin, Li, Shuping
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.12.2022; MDPI
• Subjects: Accuracy; approximate entropy; Artificial intelligence; Artificial neural networks; attention; complete ensemble empirical mode decomposition; Deep learning; Entropy; Forecasting; gated recurrent unit; Machine learning; Mathematical models; Neural networks; sample entropy; Securities markets; Signal to noise ratio; Stock exchanges; stock prediction; Time series; United States > US; attention; gated recurrent unit; approximate entropy; complete ensemble empirical mode decomposition; sample entropy; stock prediction
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25010071

Intraday stock time series are noisier and more complex than other financial time series with longer time horizons, which makes it challenging to predict. We propose a hybrid CEGH model for intraday stock market forecasting. The CEGH model contains four stages. First, we use complete ensemble empirical mode decomposition (CEEMD) to decompose the original intraday stock market data into different intrinsic mode functions (IMFs). Then, we calculate the approximate entropy (ApEn) values and sample entropy (SampEn) values of each IMF to eliminate noise. After that, we group the retained IMFs into four groups and predict the comprehensive signals of those groups using a feedforward neural network (FNN) or gate recurrent unit with history attention (GRU-HA). Finally, we obtain the final prediction results by integrating the prediction results of each group. The experiments were conducted on the U.S. and China stock markets to evaluate the proposed model. The results demonstrate that the CEGH model improved forecasting performance considerably. The creation of a collaboration between CEEMD, entropy-based denoising, and GRU-HA is our major contribution. This hybrid model could improve the signal-to-noise ratio of stock data and extract global dependence more comprehensively in intraday stock market forecasting.