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

Abstract

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.

A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN

“…Here, two-level decomposition and then entropy ratio-based denoising was performed using the CEEMDAN method. For the CEEMDAN method used in the decomposition of the stock market index data, the number of trials and white noise standard deviation values were adjusted to 200 and 0.2, respectively, taking reference from the study of Liu et al (2022b) . The values of the embedding dimension and tolerance parameters in approximate entropy and sample entropy calculated for each IMF were determined as 2 and 0.2, respectively, as a result of various experiments.…”

“…A review of literature focusing on noise reduction in time series reveals studies that cover diverse domains, including time series related to air-pollution ( Samal, Babu & Das, 2021 ), Total Column of Ozone (TCO) ( Mbatha & Bencherif, 2020 ), and electricity load/price ( Yaslan & Bican, 2017 ; Liu et al, 2019 ). Notably, a significant portion of these studies concentrates on financial time series ( Qiu, Wang & Zhou, 2020 ; Tang et al, 2021 ; Liu et al, 2022a ; Rezaei, Faaljou & Mansourfar, 2021 ; Cao, Li & Li, 2019 ; Lv et al, 2022 ; Yong’an, Yan & Aasma, 2020 ; Bao, Yue & Rao, 2017 ; Liu et al, 2022b ; Zhang et al, 2023 ). Two primary reasons underlie this situation: firstly, investors seek to enhance profit expectations through stock forecasting.…”

“…In the literature, various approaches that decompose time series into different frequency spectra, such as empirical mode decomposition (EMD) ( Zhang et al, 2023 ; Rezaei, Faaljou & Mansourfar, 2021 ; Lv et al, 2022 ), ensemble empirical mode decomposition (EEMD) ( Wu & Huang, 2009 ), variational mode decomposition (VMD) ( Wang, Cheng & Dong, 2023 ; Cui et al, 2023 ), complete ensemble empirical mode decomposition (CEEMD) ( Rezaei, Faaljou & Mansourfar, 2021 ; Yong’an, Yan & Aasma, 2020 ; Liu et al, 2022b ), and CEEMDAN ( Cao, Li & Li, 2019 ; Lv et al, 2022 ), are frequently preferred for denoising in time series. Examining studies in the literature reveals that the utilization of these approaches, combined with deep learning methods like long short-term memory (LSTM) and convolutional neural network (CNN), can mitigate the limitations of basic/single models ( Rezaei, Faaljou & Mansourfar, 2021 ; Cao, Li & Li, 2019 ; Lv et al, 2022 ; Yong’an, Yan & Aasma, 2020 ; Liu et al, 2022b ). Notably, results obtained with hybrid models, incorporating mode decomposition techniques and deep learning methods, demonstrate the superior performance of CEEMDAN-based models over others ( Cao, Li & Li, 2019 ; Lv et al, 2022 ).…”

“…Additionally, it is noteworthy that LSTM, a popular deep learning method, is frequently the method of choice in these applications ( Rezaei, Faaljou & Mansourfar, 2021 ; Cao, Li & Li, 2019 ; Yong’an, Yan & Aasma, 2020 ). However, in some studies, auto-regressive moving average (ARMA) ( Lv et al, 2022 ) and feed-forward neural networks (FFNN) ( Liu et al, 2022b ) models are utilized to capture linear components resulting from decomposition. In all these studies, it is seen that the CEEMDAN method is preferred due to its advantages.…”

A Hybrid Model Using PCA and BP Neural Network for Time Series Prediction in Chinese Stock Market with TOPSIS Analysis

“…The experiment results show that the proposed algorithm leads to better results when compared with previous methods. Liu et al [25] present a four-stage Central European Gas Hub model for intraday stock market forecasting. The results indicate that the proposed model could improve the forecasting performance compared with various baseline methods.…”