Time series predictive analysis based on hybridization of meta-heuristic algorithms
This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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International Journal on Advanced Science, Engineering and Information Technology
2018
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| Online Access: | http://eprints.uthm.edu.my/3354/ http://eprints.uthm.edu.my/3354/1/AJ%202018%20%28685%29.pdf |
| _version_ | 1848887997306503168 |
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| author | Mustaffa, Zuriani Sulaiman, Mohd Herwan Rohidin, Dede Ernawan, Ferda Kasim, Shahreen |
| author_facet | Mustaffa, Zuriani Sulaiman, Mohd Herwan Rohidin, Dede Ernawan, Ferda Kasim, Shahreen |
| author_sort | Mustaffa, Zuriani |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CSLSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities. |
| first_indexed | 2025-11-15T20:03:16Z |
| format | Article |
| id | uthm-3354 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:03:16Z |
| publishDate | 2018 |
| publisher | International Journal on Advanced Science, Engineering and Information Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-33542021-11-16T08:05:32Z http://eprints.uthm.edu.my/3354/ Time series predictive analysis based on hybridization of meta-heuristic algorithms Mustaffa, Zuriani Sulaiman, Mohd Herwan Rohidin, Dede Ernawan, Ferda Kasim, Shahreen QA71-90 Instruments and machines This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CSLSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities. International Journal on Advanced Science, Engineering and Information Technology 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/3354/1/AJ%202018%20%28685%29.pdf Mustaffa, Zuriani and Sulaiman, Mohd Herwan and Rohidin, Dede and Ernawan, Ferda and Kasim, Shahreen (2018) Time series predictive analysis based on hybridization of meta-heuristic algorithms. International Journal on Advanced Science Engineering Information Technology, 8 (5). pp. 1919-1925. ISSN 2088-5334 |
| spellingShingle | QA71-90 Instruments and machines Mustaffa, Zuriani Sulaiman, Mohd Herwan Rohidin, Dede Ernawan, Ferda Kasim, Shahreen Time series predictive analysis based on hybridization of meta-heuristic algorithms |
| title | Time series predictive analysis based on hybridization
of meta-heuristic algorithms |
| title_full | Time series predictive analysis based on hybridization
of meta-heuristic algorithms |
| title_fullStr | Time series predictive analysis based on hybridization
of meta-heuristic algorithms |
| title_full_unstemmed | Time series predictive analysis based on hybridization
of meta-heuristic algorithms |
| title_short | Time series predictive analysis based on hybridization
of meta-heuristic algorithms |
| title_sort | time series predictive analysis based on hybridization
of meta-heuristic algorithms |
| topic | QA71-90 Instruments and machines |
| url | http://eprints.uthm.edu.my/3354/ http://eprints.uthm.edu.my/3354/1/AJ%202018%20%28685%29.pdf |