Forecasting stock index price using the CEEMDAN-LSTM model

Yu, Lin; Yan, Yan; Xu, Jiali; Liao, Ying; Ma, Feng

doi:10.1016/j.najef.2021.101421

Cited by 103 publications

(39 citation statements)

References 48 publications

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“…Cao et al (2019) also find that the combined model CEEMDAN-LSTM outperforms single LSTM, MLP, and SVM. Lin et al (2021) draw the similar conclusion, suggesting CEEMDAN is an effective tool in stock indices prediction. Moreover, this paper also compares the performance of EMD-LSTM and CEEMDAN-LSTM.…”

Section: Prediction Modelssupporting

confidence: 66%

“…They find that financial news has a great influence on the volatility of stock prices, and the predicting accuracy of LSTM can go up to 96.2%. Lin et al (2021) use several models to testify whether S&P500 and CSI300 can be forecasted. The result shows that all the models are capable of predicting these stock indices, while the forecasting error rate of CEEMDAN-LSTM model is the lowest.…”

Section: Prediction Modelsmentioning

confidence: 99%

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Green Bond Index Prediction Based on CEEMDAN-LSTM

2022

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Green bonds, which are designed to finance for environment-friendly or sustainable projects, have attracted more and more investors’ attention. However, the study in this field is still relatively limited, especially in forecasting the market’s future trends. In this paper, a hybrid model combining CEEMDAN and LSTM is introduced to predict green bond market in China (represented by CUFE-CNI High Grade Green Bond Index). In order to evaluate the performance of our model, we also use EMD to decompose the green bond index. Our empirical result suggests that, compared with EMD-LSTM and LSTM models, CEEMDAN-LSTM is the most accurate model in green bond index forecasting. Meanwhile, we find that indices from the crude oil market and green stock market are both effective predictors, which also provides ground on the correlations between the green bond market and other financial markets.

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Section: Prediction Modelssupporting

confidence: 66%

Section: Prediction Modelsmentioning

confidence: 99%

Green Bond Index Prediction Based on CEEMDAN-LSTM

2022

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“…Therefore, CEEMDAN was proposed by Torres. CEEMDAN eliminates the residual white noise in the decomposition process of EEMD algorithm (Lin et al, 2021;Zhao Y et al, 2021). The following is the flow of CEEMDAN:…”

Section: Complete Ensemble Empirical Mode Decomposition With Adaptive Noisementioning

confidence: 99%

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Zhang

Chen

2021

Environ Sci Pollut Res

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In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

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“…To extract more efficient features, Guo et al ( 2020 ) applied a combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and LSTM to chaotic sequence prediction. Lin et al ( 2021 ) used the CEEMDAN-LSTM model to forecast the Chinese stock index, which proved to be the best among developed and emerging stock markets. CEEMDAN has almost zero reconstruction error by adaptively increasing and weakening the white noise, which allows it to extract more effective information.…”

Section: Introductionmentioning

confidence: 99%

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

Wang

Dong

et al. 2022

Stoch Environ Res Risk Assess

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Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM 2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM 2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning.

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Forecasting stock index price using the CEEMDAN-LSTM model

Cited by 103 publications

References 48 publications

Green Bond Index Prediction Based on CEEMDAN-LSTM

Green Bond Index Prediction Based on CEEMDAN-LSTM

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

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