Time series analysis and prediction of nonlinear systems with ensemble learning framework applied to deep learning neural networks
| Autor: | Shao-Chun Wen, Cheng-Hsiung Yang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Rössler attractor
Information Systems and Management Artificial neural network business.industry Computer science Deep learning 05 social sciences 050301 education 02 engineering and technology Lorenz system Convolutional neural network Ensemble learning Computer Science Applications Theoretical Computer Science Nonlinear system Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Time series business 0503 education Algorithm Software |
| Zdroj: | Information Sciences. 572:167-181 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2021.04.094 |
| Popis: | In this paper, we design a framework to predict the value of time series for nonlinear systems . In order to achieve this goal, many studies of applications and plans for machine learning and even deep learning become currently popular. First, we select four nonlinear systems: including a proposed four-dimensional chaotic system, Lorenz system , Duffing oscillator, and Rossler attractor . The framework has three learning parts as Long Short-Term Memory (LSTM) based on Generate Performance Model (GPM), ensemble learning based on Restrict and Control Model (RCM), and one-dimensional convolution neural network (1-DCNN) of dirichlet distribution based on Overall Verification Model (OVM). Before learning steps, we exploit K-means method as pre-processing and hypothesis verification to improve the prediction accuracy. After learning steps, we construct four forecasting progresses as Point by Point Generated Method (PPGM), Sequence Full Generated Method (SFGM), Sequence Multiple Generated Method (SMGM), and Improvement with RCM and OVM (IPRO) to predict the value of the time steps. Finally, we use Mean Average Error (MAE) as the criterion of the prediction, and estimate the accuracy by comparing the error region of the average standard deviation. |
| Databáze: | OpenAIRE |
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