Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models
The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multist...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/567246 |
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| _version_ | 1849690667452727296 |
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| author | Wei Ming Yukun Bao Zhongyi Hu Tao Xiong |
| author_facet | Wei Ming Yukun Bao Zhongyi Hu Tao Xiong |
| author_sort | Wei Ming |
| collection | DOAJ |
| description | The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines’ monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy. |
| format | Article |
| id | doaj-art-eeed4fbe983d49579ab6ed6da427f87f |
| institution | DOAJ |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-eeed4fbe983d49579ab6ed6da427f87f2025-08-20T03:21:15ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/567246567246Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs ModelsWei Ming0Yukun Bao1Zhongyi Hu2Tao Xiong3Department of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines’ monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy.http://dx.doi.org/10.1155/2014/567246 |
| spellingShingle | Wei Ming Yukun Bao Zhongyi Hu Tao Xiong Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models The Scientific World Journal |
| title | Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models |
| title_full | Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models |
| title_fullStr | Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models |
| title_full_unstemmed | Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models |
| title_short | Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models |
| title_sort | multistep ahead air passengers traffic prediction with hybrid arima svms models |
| url | http://dx.doi.org/10.1155/2014/567246 |
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