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...
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| Format: | Article |
| Language: | English |
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Indonesian Society for Knowledge and Human Development
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30076/ http://umpir.ump.edu.my/id/eprint/30076/1/4968-15300-1-PB.pdf |
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| author | Zuriani, Mustaffa M. H., Sulaiman Rohidin, Dede Ernawan, Ferda Shahreen, Kasim |
| author_facet | Zuriani, Mustaffa M. H., Sulaiman Rohidin, Dede Ernawan, Ferda Shahreen, Kasim |
| author_sort | Zuriani, Mustaffa |
| building | UMP 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, CS-LSSVM, 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 | 2025-11-15T02:56:59Z |
| format | Article |
| id | ump-30076 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:56:59Z |
| publishDate | 2018 |
| publisher | Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-300762021-10-25T04:17:08Z http://umpir.ump.edu.my/id/eprint/30076/ Time series predictive analysis based on hybridization of meta-heuristic algorithms Zuriani, Mustaffa M. H., Sulaiman Rohidin, Dede Ernawan, Ferda Shahreen, Kasim QA75 Electronic computers. Computer science 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, CS-LSSVM, 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. Indonesian Society for Knowledge and Human Development 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30076/1/4968-15300-1-PB.pdf Zuriani, Mustaffa and M. H., Sulaiman and Rohidin, Dede and Ernawan, Ferda and Shahreen, Kasim (2018) Time series predictive analysis based on hybridization of meta-heuristic algorithms. International Journal on Advanced Science, Engineering and Information Technology, 8 (5). pp. 1919-1925. ISSN 2088-5334. (Published) https://doi.org/10.18517/ijaseit.8.5.4968 https://doi.org/10.18517/ijaseit.8.5.4968 |
| spellingShingle | QA75 Electronic computers. Computer science Zuriani, Mustaffa M. H., Sulaiman Rohidin, Dede Ernawan, Ferda Shahreen, Kasim 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 | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/30076/ http://umpir.ump.edu.my/id/eprint/30076/ http://umpir.ump.edu.my/id/eprint/30076/ http://umpir.ump.edu.my/id/eprint/30076/1/4968-15300-1-PB.pdf |