Financial time series forecasting model based on CEEMDAN and LSTM

Cao, Jian; Li, Bingbing; Li, Jian

doi:10.1016/j.physa.2018.11.061

Cited by 503 publications

(223 citation statements)

References 25 publications

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“…Second, a dynamic early warning system is developed integrating the crisis classifier and long short-term memory (LSTM) neural network (Jordan, 1997) to alert crisis onsets. As for the predictive model, LSTM is proven to be a state-of-art mechanism in the general field of financial forecasting Fischer and Krauss, 2018;Wu and Gao, 2018;Cao et al, 2019), including volatility forecasting (Yu and Li, 2018;Kim and Won, 2018;Liu, 2019). To the best of the authors' knowledge, this study is the first that incorporates LSTM in an EWS.…”

Section: Introductionmentioning

confidence: 99%

An integrated early warning system for stock market turbulence

Wang

Zong

2020

Expert Systems with Applications

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This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. An hybrid algorithm is then developed in the framework of a long short-term memory (LSTM) network to make daily predictions that alert turmoils. In the empirical evaluation based on ten-year Chinese stock data, the proposed EWS yields satisfying results with the test-set accuracy of 96.6% and on average 2.4 days of forewarned period. The model's stability and practical value in the real-time decision-making are also proven by the cross-validation and back-testing.

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Section: Introductionmentioning

confidence: 99%

An integrated early warning system for stock market turbulence

Wang

Zong

2020

Expert Systems with Applications

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“…To predict the daily closing prices of major global stock indices, Cao, Li, and Li (2019) developed a decomposition-ensemble approach based on CEEMDAN and the long short-term memory (LSTM) model. Similarly, the approach includes three main steps, as follows: (1) First, the time series of a stock index is decomposed into several intrinsic mode functions and one residue by CEEMDAN.…”

Section: Ceemdan-based Decomposition-ensemble Approachesmentioning

confidence: 99%

A decomposition-ensemble approach for tourism forecasting

Xie

Qian

Wang

2020

Annals of Tourism Research

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A B S T R A C TWith the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.

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“…Although EMD method has significant advantages in analyzing the nonstationary signal, there are some inherent limitations that make great influence on the performance of EMD, such as the mode mixing problem and the end-point effect [31]. In order to eliminate these problems in EMD, a noise-assisted signal analysis approach, named "ensemble empirical mode decomposition (EEMD)," was developed by Wu and Huang in 2009 [33].…”

Section: Complete Ensemble Empirical Mode Decomposition Withmentioning

confidence: 99%

“…Various decomposition methods, such as wavelet transform (WT) [26], empirical mode decomposition (EMD) [27], and ensemble empirical mode decomposition (EEMD) [28], are adopted to decompose the original data series to reduce the influence of irregular volatility on forecasting results. Compared with the approaches above, a new type of decomposition method, named "complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)," has attracted a huge amount of attention due to its excellent performance and high efficiency [29][30][31]. Qu developed a wind speed forecasting method based on CEEMDAN and an improved backpropagation neural network (BPNN), and the experiment results indicated that CEEMDAN could efficiently solve the problem of data fluctuations [30].…”

Section: Introductionmentioning

confidence: 99%

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

et al. 2019

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As an essential component and core power source of aircraft, the operational stability of aeroengine has important impact on system safety and reliability. Accurate degradation tendency prediction on an engine can not only improve its operational stability but also significantly reduce the maintenance costs. In this paper, a novel forecasting method that combines CEEMDAN permutation entropy and improved Grey–Markov model is proposed to perform multistep degradation tendency prediction of aircraft engines. In order to accurately quantify the degradation level of engines, a new integrated degradation index (IDI) is innovatively designed by multidimensional sensory data. And then, because of high speed and excellent performance, CEEMDAN algorithm is specifically employed to decompose the generated IDI series to eliminate the potential influence of stochastic fluctuations. Aiming at the complexity of intrinsic mode functions (IMFs) generated by CEEMDAN, an IMFs reconstruction strategy based on permutation entropy is developed to better characterize the degradation states. Finally, on the basis of above achievements and for higher forecasting efficiency and accuracy, an improved Grey–Markov model combined with the moving window algorithm, which is unique, is constructed to realize multistep degradation trend prediction of engines. The proposed method is applied to the degradation tendency prediction of aircraft engines. The experimental results validate the effectiveness and superiority of the proposed method, and it is more suitable for engineering applications in comparison with other methods.

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Financial time series forecasting model based on CEEMDAN and LSTM

Cited by 503 publications

References 25 publications

An integrated early warning system for stock market turbulence

An integrated early warning system for stock market turbulence

A decomposition-ensemble approach for tourism forecasting

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

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