Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; howeve...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/14/5/955 |
| _version_ | 1797495148423151616 |
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| author | Bhukya Ramadevi Kishore Bingi |
| author_facet | Bhukya Ramadevi Kishore Bingi |
| author_sort | Bhukya Ramadevi |
| collection | DOAJ |
| description | Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed. |
| first_indexed | 2024-03-10T01:44:18Z |
| format | Article |
| id | doaj.art-7da305a22a3343b9bcb8149c27a63c0c |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T01:44:18Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-7da305a22a3343b9bcb8149c27a63c0c2023-11-23T13:18:53ZengMDPI AGSymmetry2073-89942022-05-0114595510.3390/sym14050955Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A ReviewBhukya Ramadevi0Kishore Bingi1School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaTraditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.https://www.mdpi.com/2073-8994/14/5/955chaosforecastinghydrological systemsneural networksoil and gaspower and energy |
| spellingShingle | Bhukya Ramadevi Kishore Bingi Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review Symmetry chaos forecasting hydrological systems neural networks oil and gas power and energy |
| title | Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review |
| title_full | Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review |
| title_fullStr | Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review |
| title_full_unstemmed | Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review |
| title_short | Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review |
| title_sort | chaotic time series forecasting approaches using machine learning techniques a review |
| topic | chaos forecasting hydrological systems neural networks oil and gas power and energy |
| url | https://www.mdpi.com/2073-8994/14/5/955 |
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