Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting
Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper pro...
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Online Access: | http://dx.doi.org/10.1155/2021/8810046 |
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doaj-5ad5b858b08c4f6cbab1a791826559d02021-06-28T01:50:58ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52732021-01-01202110.1155/2021/8810046Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data ForecastingXue-Bo Jin0Jia-Hui Zhang1Ting-Li Su2Yu-Ting Bai3Jian-Lei Kong4Xiao-Yi Wang5School of Artificial IntelligenceSchool of Artificial IntelligenceSchool of Artificial IntelligenceSchool of Artificial IntelligenceSchool of Artificial IntelligenceSchool of Artificial IntelligenceComplex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.http://dx.doi.org/10.1155/2021/8810046 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xue-Bo Jin Jia-Hui Zhang Ting-Li Su Yu-Ting Bai Jian-Lei Kong Xiao-Yi Wang |
spellingShingle |
Xue-Bo Jin Jia-Hui Zhang Ting-Li Su Yu-Ting Bai Jian-Lei Kong Xiao-Yi Wang Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting Computational Intelligence and Neuroscience |
author_facet |
Xue-Bo Jin Jia-Hui Zhang Ting-Li Su Yu-Ting Bai Jian-Lei Kong Xiao-Yi Wang |
author_sort |
Xue-Bo Jin |
title |
Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting |
title_short |
Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting |
title_full |
Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting |
title_fullStr |
Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting |
title_full_unstemmed |
Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting |
title_sort |
modeling and analysis of data-driven systems through computational neuroscience wavelet-deep optimized model for nonlinear multicomponent data forecasting |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5273 |
publishDate |
2021-01-01 |
description |
Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series. |
url |
http://dx.doi.org/10.1155/2021/8810046 |
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