Chaotic Time Series Prediction Using LSTM with CEEMDAN

Chaotic systems are complex dynamical systems that play a very important role in the study of the atmosphere, aerospace engineering, finance, etc. To improve the accuracy of chaotic time series prediction, this study proposes a hybrid model CEEMDAN-LSTM which combines Complete Ensemble Empirical Mod...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1617; no. 1; pp. 12094 - 12099
Main Authors Yanan, Guo, Xiaoqun, Cao, Bainian, Liu, Kecheng, Peng
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.08.2020
Subjects
Aerospace engineering
Atmospheric models
Autoregressive models
Chaos theory
Decomposition
Multilayer perceptrons
Physics
Prediction models
Predictions
Sequences
Statistical analysis
Support vector machines
Time series
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Summary:Chaotic systems are complex dynamical systems that play a very important role in the study of the atmosphere, aerospace engineering, finance, etc. To improve the accuracy of chaotic time series prediction, this study proposes a hybrid model CEEMDAN-LSTM which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and long short-term memory (LSTM). In the model, the original time series is decomposed into several intrinsic mode functions (IMFs) and a residual component. To reduce the difficulty of predicting chaotic time series and provide a high level of predictive accuracy, the LSTM prediction model is built for all each characteristic series from CEEMDAN deposition. Finally, the final prediction results are obtained by combining all the prediction sequences. To test the effectiveness of this model we proposed, we examined the CEEMDAN-LSTM model using the Lorenz-63 system. Further compared to Autoregressive Integrated Moving Average (ARIMA ), Support Vector Regression (SVR), multilayer perceptron (MLP), and the single LSTM model, the results of the experiment show that the proposed model performs better in the prediction of chaotic time series. Besides, the hybrid model proposed in this paper has better results than the LSTM model alone. Therefore, hybrid models based on deep learning methods and signal decomposition methods have great potential in the field of chaotic time series prediction.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1617/1/012094