A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification
Time series classification (TSC) is essential in various application domains to understand the system dynamics. The adoption of deep learning has advanced TSC, however its performance is sensitive to hyperparameters configuration. Manual tuning of high-dimensional hyperparameters can be labor intens...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10795132/ |
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| author | Ayuningtyas Hari Fristiana Syukron Abu Ishaq Alfarozi Adhistya Erna Permanasari Mahardhika Pratama Sunu Wibirama |
| author_facet | Ayuningtyas Hari Fristiana Syukron Abu Ishaq Alfarozi Adhistya Erna Permanasari Mahardhika Pratama Sunu Wibirama |
| author_sort | Ayuningtyas Hari Fristiana |
| collection | DOAJ |
| description | Time series classification (TSC) is essential in various application domains to understand the system dynamics. The adoption of deep learning has advanced TSC, however its performance is sensitive to hyperparameters configuration. Manual tuning of high-dimensional hyperparameters can be labor intensive, leading to a preference for automatic hyperparameters optimization (HPO) methods. To the best of our knowledge, survey papers covering various studies on automatic hyperparameters optimization (HPO) of deep learning for TSC are scarce and even none. To address this gap, we present a systematic literature review to assist researchers in addressing the HPO problem for deep learning in TSC. We analyzed studies published between 2018 and June 2024. This review examines the HPO methods, hyperparameters, and tools utilized in this context based on 77 primary studies sourced from academic databases. The findings indicate that Metaheuristic algorithm and Bayesian Optimization are commonly employed approaches, with a focus on hyperparameters related to the deep learning architectures. This review provides insights that can inform the design and implementation of HPO strategies for deep learning models in time series analysis. |
| format | Article |
| id | doaj-art-52f7a32f5d3d4a9da698b39a50e68ef1 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-52f7a32f5d3d4a9da698b39a50e68ef12025-08-20T01:57:08ZengIEEEIEEE Access2169-35362024-01-011219116219119810.1109/ACCESS.2024.351619810795132A Survey on Hyperparameters Optimization of Deep Learning for Time Series ClassificationAyuningtyas Hari Fristiana0Syukron Abu Ishaq Alfarozi1https://orcid.org/0000-0001-8558-898XAdhistya Erna Permanasari2https://orcid.org/0000-0002-2663-4074Mahardhika Pratama3https://orcid.org/0000-0001-6531-5087Sunu Wibirama4https://orcid.org/0000-0001-9613-7017Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, IndonesiaDepartment of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, IndonesiaDepartment of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, IndonesiaSTEM, University of South Australia, Adelaide, SA, AustraliaDepartment of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, IndonesiaTime series classification (TSC) is essential in various application domains to understand the system dynamics. The adoption of deep learning has advanced TSC, however its performance is sensitive to hyperparameters configuration. Manual tuning of high-dimensional hyperparameters can be labor intensive, leading to a preference for automatic hyperparameters optimization (HPO) methods. To the best of our knowledge, survey papers covering various studies on automatic hyperparameters optimization (HPO) of deep learning for TSC are scarce and even none. To address this gap, we present a systematic literature review to assist researchers in addressing the HPO problem for deep learning in TSC. We analyzed studies published between 2018 and June 2024. This review examines the HPO methods, hyperparameters, and tools utilized in this context based on 77 primary studies sourced from academic databases. The findings indicate that Metaheuristic algorithm and Bayesian Optimization are commonly employed approaches, with a focus on hyperparameters related to the deep learning architectures. This review provides insights that can inform the design and implementation of HPO strategies for deep learning models in time series analysis.https://ieeexplore.ieee.org/document/10795132/Deep neural networkhyperparameters tuningevolutionary algorithmtime series analysisBayesian optimizationmultifidelity algorithm |
| spellingShingle | Ayuningtyas Hari Fristiana Syukron Abu Ishaq Alfarozi Adhistya Erna Permanasari Mahardhika Pratama Sunu Wibirama A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification IEEE Access Deep neural network hyperparameters tuning evolutionary algorithm time series analysis Bayesian optimization multifidelity algorithm |
| title | A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification |
| title_full | A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification |
| title_fullStr | A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification |
| title_full_unstemmed | A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification |
| title_short | A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification |
| title_sort | survey on hyperparameters optimization of deep learning for time series classification |
| topic | Deep neural network hyperparameters tuning evolutionary algorithm time series analysis Bayesian optimization multifidelity algorithm |
| url | https://ieeexplore.ieee.org/document/10795132/ |
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