Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer
The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices. In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for s...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2015
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| Online Access: | http://umpir.ump.edu.my/id/eprint/8968/1/fskkp-2015-zuriani-time%20series%20forecasting.pdf http://umpir.ump.edu.my/id/eprint/8968/ http://www.iaeng.org/publication/IMECS2015/IMECS2015_pp25-30.pdf |
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| author | Zuriani, Mustaffa Yuhanis, Yusof |
| author_facet | Zuriani, Mustaffa Yuhanis, Yusof |
| author_sort | Zuriani, Mustaffa |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices. In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting. The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price. Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE). Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. Furthermore, the proposed GWO produces a better forecast for gasoline price as compared to the ABC model,, as well as being at par in crude oil. Such an achievement indicates that GWO may become a competitor in the domain of time series forecasting and would be useful for investors in planning their investment and projecting their profit. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.8968 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2015 |
| record_format | eprints |
| spelling | my.ump.umpir.89682018-02-26T07:58:50Z http://umpir.ump.edu.my/id/eprint/8968/ Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer Zuriani, Mustaffa Yuhanis, Yusof QA76 Computer software The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices. In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting. The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price. Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE). Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. Furthermore, the proposed GWO produces a better forecast for gasoline price as compared to the ABC model,, as well as being at par in crude oil. Such an achievement indicates that GWO may become a competitor in the domain of time series forecasting and would be useful for investors in planning their investment and projecting their profit. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8968/1/fskkp-2015-zuriani-time%20series%20forecasting.pdf Zuriani, Mustaffa and Yuhanis, Yusof (2015) Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer. In: Proceedings of the International MultiConference of Engineers and Computer Scientist (IMECS 2015) , 18-20 March 2015 , Hong Kong. pp. 25-30.. (Published) http://www.iaeng.org/publication/IMECS2015/IMECS2015_pp25-30.pdf |
| spellingShingle | QA76 Computer software Zuriani, Mustaffa Yuhanis, Yusof Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer |
| title | Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer |
| title_full | Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer |
| title_fullStr | Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer |
| title_full_unstemmed | Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer |
| title_short | Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer |
| title_sort | time series forecasting of energy commodity using grey wolf optimizer |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/8968/1/fskkp-2015-zuriani-time%20series%20forecasting.pdf http://umpir.ump.edu.my/id/eprint/8968/ http://www.iaeng.org/publication/IMECS2015/IMECS2015_pp25-30.pdf |
| url_provider | http://umpir.ump.edu.my/ |