Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting
In the evaluation of supply chain process improvements, the question of how to predict product demand quantity and prepare material flows in order to reduce cycle time has emerged as an important issue, especially in the 3C (computer, communication, and consumer electronic) market. This paper constr...
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| Veröffentlicht in: | International journal of production economics 2010-12, Vol.128 (2), p.603-613 |
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| container_title | International journal of production economics |
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| creator | Lu, Chi-Jie Wang, Yen-Wen |
| description | In the evaluation of supply chain process improvements, the question of how to predict product demand quantity and prepare material flows in order to reduce cycle time has emerged as an important issue, especially in the 3C (computer, communication, and consumer electronic) market. This paper constructs a predicting model to deal with the product demand forecast problem with the aid of a growing hierarchical self-organizing maps and independent component analysis. Independent component analysis method is used to detect and remove the noise of data and further improve the performance of predicting model, then growing hierarchical self-organizing maps is used to classify the data, and after the classification, support vector regression is applied to construct the product demand forecasting model. In the experimental results, the model proposed in this paper can be successfully applied in the forecasting problem. |
| doi_str_mv | 10.1016/j.ijpe.2010.07.004 |
| format | Article |
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In the experimental results, the model proposed in this paper can be successfully applied in the forecasting problem.</description><identifier>ISSN: 0925-5273</identifier><identifier>EISSN: 1873-7579</identifier><identifier>DOI: 10.1016/j.ijpe.2010.07.004</identifier><identifier>CODEN: IJPCEY</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Demand ; Demand analysis ; Demand forecasting ; Demand forecasting Support vector regression Independent component analysis Growing hierarchical self-organizing maps ; Forecasting ; Forecasting techniques ; Growing hierarchical self-organizing maps ; Independent component analysis ; Marketing ; Mathematical analysis ; Mathematical models ; Operations research ; Principal components analysis ; Regression ; Studies ; Supply chain management ; Support vector regression</subject><ispartof>International journal of production economics, 2010-12, Vol.128 (2), p.603-613</ispartof><rights>2010 Elsevier B.V.</rights><rights>Copyright Elsevier Sequoia S.A. 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This paper constructs a predicting model to deal with the product demand forecast problem with the aid of a growing hierarchical self-organizing maps and independent component analysis. Independent component analysis method is used to detect and remove the noise of data and further improve the performance of predicting model, then growing hierarchical self-organizing maps is used to classify the data, and after the classification, support vector regression is applied to construct the product demand forecasting model. In the experimental results, the model proposed in this paper can be successfully applied in the forecasting problem.</description><subject>Demand</subject><subject>Demand analysis</subject><subject>Demand forecasting</subject><subject>Demand forecasting Support vector regression Independent component analysis Growing hierarchical self-organizing maps</subject><subject>Forecasting</subject><subject>Forecasting techniques</subject><subject>Growing hierarchical self-organizing maps</subject><subject>Independent component analysis</subject><subject>Marketing</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Operations research</subject><subject>Principal components analysis</subject><subject>Regression</subject><subject>Studies</subject><subject>Supply chain management</subject><subject>Support vector regression</subject><issn>0925-5273</issn><issn>1873-7579</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9UU2v0zAQjBBIlAd_gJPFiUuKPxLblbigigeIJ3GBs-Vnb1pXiR1sp0_lb_CH2VDEgQOHtVf2zOxqpmleMrpllMk3p204zbDlFB-o2lLaPWo2TCvRql7tHjcbuuN923MlnjbPSjlRShXTetP83KfpPsQQDyREDzPgEStxaZpTXDsb7XgpoWDjySGnhxV6DJBtdsfg7EgKjEOb8sHG8GP9nOxcyEOoR1KWeU65kjO4mjLJcMhQSkgRZ5E5J7-4SjxMq_SQMjhbKio8b54Mdizw4s9903y7ff91_7G9-_Lh0_7dXeu6HastSM07xrViXmoFnR2cVpxBTzspJeWeyl7LnlHnhWU96--ZE3aQVljqlaLipnl91cVVvi9QqplCcTCONkJaimFSMcGlZjuEvvoHekpLRmuK0VTJrut4jyB-BbmcSskwmDmHyeaLYdSsMZmTWWMya0yGKoMxIenzlZTRfPeXAQA4ClwyZ4Pbc43nBes3VdiwtlgzlqTCSCbMsU6o9vaqBujbGVMyxQWIDnxAe6vxKfxvmV-3C7es</recordid><startdate>20101201</startdate><enddate>20101201</enddate><creator>Lu, Chi-Jie</creator><creator>Wang, Yen-Wen</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TA</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>KR7</scope></search><sort><creationdate>20101201</creationdate><title>Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting</title><author>Lu, Chi-Jie ; Wang, Yen-Wen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c491t-e682412871d687e4afc8721e50466602d06586510cd3a1515b1c3af6a3a0d7703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Demand</topic><topic>Demand analysis</topic><topic>Demand forecasting</topic><topic>Demand forecasting Support vector regression Independent component analysis Growing hierarchical self-organizing maps</topic><topic>Forecasting</topic><topic>Forecasting techniques</topic><topic>Growing hierarchical self-organizing maps</topic><topic>Independent component analysis</topic><topic>Marketing</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Operations research</topic><topic>Principal components analysis</topic><topic>Regression</topic><topic>Studies</topic><topic>Supply chain management</topic><topic>Support vector regression</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lu, Chi-Jie</creatorcontrib><creatorcontrib>Wang, Yen-Wen</creatorcontrib><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>International journal of production economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lu, Chi-Jie</au><au>Wang, Yen-Wen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting</atitle><jtitle>International journal of production economics</jtitle><date>2010-12-01</date><risdate>2010</risdate><volume>128</volume><issue>2</issue><spage>603</spage><epage>613</epage><pages>603-613</pages><issn>0925-5273</issn><eissn>1873-7579</eissn><coden>IJPCEY</coden><abstract>In the evaluation of supply chain process improvements, the question of how to predict product demand quantity and prepare material flows in order to reduce cycle time has emerged as an important issue, especially in the 3C (computer, communication, and consumer electronic) market. 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| ispartof | International journal of production economics, 2010-12, Vol.128 (2), p.603-613 |
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| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Demand Demand analysis Demand forecasting Demand forecasting Support vector regression Independent component analysis Growing hierarchical self-organizing maps Forecasting Forecasting techniques Growing hierarchical self-organizing maps Independent component analysis Marketing Mathematical analysis Mathematical models Operations research Principal components analysis Regression Studies Supply chain management Support vector regression |
| title | Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting |
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