Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effect...
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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/10.1155/2014/438132 |
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| author | Wensheng Dai Jui-Yu Wu Chi-Jie Lu |
| author_facet | Wensheng Dai Jui-Yu Wu Chi-Jie Lu |
| author_sort | Wensheng Dai |
| collection | DOAJ |
| description | Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. |
| format | Article |
| id | doaj-art-26f155adf85547a8a74d891e35ee9a17 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-26f155adf85547a8a74d891e35ee9a172025-08-20T03:54:48ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/438132438132Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales ForecastingWensheng Dai0Jui-Yu Wu1Chi-Jie Lu2International Monetary Institute, Financial School, Renmin University of China, Beijing 100872, ChinaDepartment of Business Administration, Lunghwa University of Science and Technology, Taoyuan County 33306, TaiwanDepartment of Industrial Management, Chien Hsin University of Science and Technology, Taoyuan County 32097, TaiwanSales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.http://dx.doi.org/10.1155/2014/438132 |
| spellingShingle | Wensheng Dai Jui-Yu Wu Chi-Jie Lu Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting The Scientific World Journal |
| title | Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting |
| title_full | Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting |
| title_fullStr | Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting |
| title_full_unstemmed | Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting |
| title_short | Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting |
| title_sort | applying different independent component analysis algorithms and support vector regression for it chain store sales forecasting |
| url | http://dx.doi.org/10.1155/2014/438132 |
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