GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction

Qi, Chenyang; Ren, Jie; Su, Jin

doi:10.3390/app13127104

Cited by 12 publications

(10 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model was applied to the ARMA approach on the IMF, which is more linear as a result of denoising, and the LSTM method was applied to the remaining ones. Finally, the GRU based on CEEMDAN-Wavelet forecasting model ( Qi, Ren & Su, 2023 ) used a combination of decomposition and wavelet transformation for denoising financial time series. The wavelet threshold denoising method was applied to the IMFs obtained from the decomposition, and these IMFs were then reconstructed.…”

Section: Resultsmentioning

confidence: 99%

“…The wavelet transform-based denoising approach has been applied to eliminate noisy components in different stock market data, and subsequently, LSTM models were developed on the resulting noiseless data ( Dastgerdi & Mercorelli, 2022 ; Tang et al, 2021 ; Bao, Yue & Rao, 2017 ). A hybrid noise reduction approach, combining the wavelet transform with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods, was proposed to enhance further noise reduction in financial time series data ( Qi, Ren & Su, 2023 ). While experimental results in these studies indicate a significant improvement in prediction stability through noise reduction using the wavelet transform, the method has some limitations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Akşehir,

Kılıç

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Time series, including noise, non-linearity, and non-stationary properties, are frequently used in prediction problems. Due to these inherent characteristics of time series data, forecasting based on this data type is a highly challenging problem. In many studies within the literature, high-frequency components are commonly excluded from time series data. However, these high-frequency components can contain valuable information, and their removal may adversely impact the prediction performance of models. In this study, a novel method called Two-Level Entropy Ratio-Based Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (2LE-CEEMDAN) is proposed for the first time to effectively denoise time series data. Financial time series with high noise levels are utilized to validate the effectiveness of the proposed method. The 2LE-CEEMDAN-LSTM-SVR model is introduced to predict the next day’s closing value of stock market indices within the scope of financial time series. This model comprises two main components: denoising and forecasting. In the denoising section, the proposed 2LE-CEEMDAN method eliminates noise in financial time series, resulting in denoised intrinsic mode functions (IMFs). In the forecasting part, the next-day value of the indices is estimated by training on the denoised IMFs obtained. Two different artificial intelligence methods, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), are utilized during the training process. The IMF, characterized by more linear characteristics than the denoised IMFs, is trained using the SVR, while the others are trained using the LSTM method. The final prediction result of the 2LE-CEEMDAN-LSTM-SVR model is obtained by integrating the prediction results of each IMF. Experimental results demonstrate that the proposed 2LE-CEEMDAN denoising method positively influences the model’s prediction performance, and the 2LE-CEEMDAN-LSTM-SVR model outperforms other prediction models in the existing literature.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Akşehir,

Kılıç

2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The wheel diameter, flange thickness, and flange height values were predicted for approximately one year. These predictions are then analyzed in comparison with predictions from the BP (back propagation) neural network prediction model and the GRU (gate recurrent unit) neural network prediction model [26][27][28][29]. The comparison between the predicted results and the actual values is shown in Figures 13 and 14.…”

Section: Model Prediction Results and Comparative Analysismentioning

confidence: 99%

Prediction of Key Parameters of Wheelset Based on LSTM Neural Network

Ye,

Wen,

Zheng

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

As a key component of rail vehicle operation, the running condition of the wheelset significantly affects the operational safety of track vehicles. The wheel diameter, flange thickness, and flange height are key dimensional parameters of the wheelset, which directly influence the correct position of wheelsets on the track, and the train needs to be continuously monitored during the passenger operation. A prediction model for the key parameters of the wheelset is established based on LSTM (long short-term memory) neural network, and real measured data of wheelsets from the Shanghai Metro vehicles are selected. The predicted results of the model are compared and analyzed, and the results show that the LSTM-based prediction model for key parameters of wheelsets performs well, with the mean absolute percentage errors (MAPEs) for wheel diameter, flange thickness, and flange height being 0.08%, 0.42%, and 0.44%, respectively, for the left wheel and 0.07%, 0.35%, and 0.44%, respectively, for the right wheel. The prediction model for the train wheelset parameters established in this paper has a good prediction accuracy. By predicting the key parameters of the wheelset, the faults and causes of the wheelset can be found and determined, which is helpful for engineers to overhaul the wheelset faults, make maintenance plans, and perform preventive maintenance.

show abstract

“…GRU was utilized by Ya Gao et al [6] to predict stocks. GRU Neural Network Based on CEEMDAN-Wavelet was utilized by Chenyang Qi et al [7] to predict stock prices.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Financial Markets Utilizing an Innovatively Optimized Hybrid Model: A Case Study of the Hang Seng Index

YANG

2024

IJACSA

View full text Add to dashboard Cite

Stock trading is a highly consequential and frequently discussed subject in the realm of financial markets. Due to the volatile and unpredictable nature of stock prices, investors are perpetually seeking methods to forecast future trends in order to minimize losses and maximize profits. Nevertheless, despite the ongoing investigation of various approaches to optimize the predictive efficacy of models, it is indisputable that a method for accurately forecasting forthcoming market trends does not yet exist. A multitude of algorithms are currently being employed to forecast stock prices due to significant developments that have occurred in recent years. An innovative algorithm for predicting stock prices are examined in this paper which is a Gated Recurrent Unit combined with the Aquila optimizer. A comprehensive data implementation utilizing the Hang Seng Index stock price was executed as a dataset of this research which was collected between the years of 2015 and the end of June 2023. In the study, several additional methods for predicting stock market movements are also detailed. A comprehensive comparative analysis of the stock price prediction performances of the aforementioned algorithms has also been carried out to offer a more in-depth analysis and then the results are displayed in an understandable tabular and graphical manner. The proposed model obtained the values of 0.9934, 0.71, 143.62, and 36530.58, for 𝑹 𝟐 , MAPE, MAE, and MSE, respectively. These results proved the efficiency and accuracy of the suggested method and it was determined that the proposed model algorithm produces results with a high degree of accuracy and performs the best when it comes to forecasting a time series or stock price.

show abstract

GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction

Cited by 12 publications

References 44 publications

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

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

Prediction of Key Parameters of Wheelset Based on LSTM Neural Network

Prediction of Financial Markets Utilizing an Innovatively Optimized Hybrid Model: A Case Study of the Hang Seng Index

Contact Info

Product

Resources

About