Forecasting Time Series Movement Direction with Hybrid Methodology
Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed mod...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2017/3174305 |
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| _version_ | 1850168247280730112 |
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| author | Salwa Waeto Khanchit Chuarkham Arthit Intarasit |
| author_facet | Salwa Waeto Khanchit Chuarkham Arthit Intarasit |
| author_sort | Salwa Waeto |
| collection | DOAJ |
| description | Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error. |
| format | Article |
| id | doaj-art-e14022b8f12347f188860d231a9cfaf2 |
| institution | OA Journals |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-e14022b8f12347f188860d231a9cfaf22025-08-20T02:21:01ZengWileyJournal of Probability and Statistics1687-952X1687-95382017-01-01201710.1155/2017/31743053174305Forecasting Time Series Movement Direction with Hybrid MethodologySalwa Waeto0Khanchit Chuarkham1Arthit Intarasit2Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani 94000, ThailandFaculty of Commerce and Management, Prince of Songkla University, Trang Campus, Trang 92000, ThailandDepartment of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani 94000, ThailandForecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error.http://dx.doi.org/10.1155/2017/3174305 |
| spellingShingle | Salwa Waeto Khanchit Chuarkham Arthit Intarasit Forecasting Time Series Movement Direction with Hybrid Methodology Journal of Probability and Statistics |
| title | Forecasting Time Series Movement Direction with Hybrid Methodology |
| title_full | Forecasting Time Series Movement Direction with Hybrid Methodology |
| title_fullStr | Forecasting Time Series Movement Direction with Hybrid Methodology |
| title_full_unstemmed | Forecasting Time Series Movement Direction with Hybrid Methodology |
| title_short | Forecasting Time Series Movement Direction with Hybrid Methodology |
| title_sort | forecasting time series movement direction with hybrid methodology |
| url | http://dx.doi.org/10.1155/2017/3174305 |
| work_keys_str_mv | AT salwawaeto forecastingtimeseriesmovementdirectionwithhybridmethodology AT khanchitchuarkham forecastingtimeseriesmovementdirectionwithhybridmethodology AT arthitintarasit forecastingtimeseriesmovementdirectionwithhybridmethodology |