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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Indonesian Society for Knowledge and Human Development
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30076/1/4968-15300-1-PB.pdf http://umpir.ump.edu.my/id/eprint/30076/ https://doi.org/10.18517/ijaseit.8.5.4968 https://doi.org/10.18517/ijaseit.8.5.4968 |
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| Summary: | 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. |
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