A New Method for Crude Oil Price Forecasting Based on Support Vector Machines
This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evalu...
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| creator | Xie, Wen Yu, Lean Xu, Shanying Wang, Shouyang |
| description | This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The experiment results show that SVM outperforms the other two methods and is a fairly good candidate for the crude oil price prediction. |
| doi_str_mv | 10.1007/11758549_63 |
| format | Book Chapter |
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The experiment results show that SVM outperforms the other two methods and is a fairly good candidate for the crude oil price prediction.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540343851</identifier><identifier>ISBN: 3540343857</identifier><identifier>ISBN: 3540343792</identifier><identifier>ISBN: 9783540343790</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540343868</identifier><identifier>EISBN: 3540343865</identifier><identifier>DOI: 10.1007/11758549_63</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; ARIMA Model ; Computer science; control theory; systems ; Data processing. List processing. Character string processing ; Exact sciences and technology ; Memory organisation. 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A.</contributor><creatorcontrib>Xie, Wen</creatorcontrib><creatorcontrib>Yu, Lean</creatorcontrib><creatorcontrib>Xu, Shanying</creatorcontrib><creatorcontrib>Wang, Shouyang</creatorcontrib><title>A New Method for Crude Oil Price Forecasting Based on Support Vector Machines</title><title>Computational Science – ICCS 2006</title><description>This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The experiment results show that SVM outperforms the other two methods and is a fairly good candidate for the crude oil price prediction.</description><subject>Applied sciences</subject><subject>ARIMA Model</subject><subject>Computer science; control theory; systems</subject><subject>Data processing. List processing. Character string processing</subject><subject>Exact sciences and technology</subject><subject>Memory organisation. Data processing</subject><subject>Root Mean Square Error</subject><subject>Software</subject><subject>Support Vector Machine</subject><subject>Support Vector Machine Model</subject><subject>Support Vector Regression</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540343851</isbn><isbn>3540343857</isbn><isbn>3540343792</isbn><isbn>9783540343790</isbn><isbn>9783540343868</isbn><isbn>3540343865</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><recordid>eNpVkDtPwzAUhc1Loiqd-ANeGBgCdu61Y4-looBEKRKPNXJspw2UOLJTIf49QWWAs5zhPIaPkFPOLjhjxSXnhVACdSlhj0x0oUAgAwQl1T4Zccl5BoD64F8m-CEZMWB5pguEYzJJ6Y0NAi5Vno_IYkof_Cdd-H4dHK1DpLO4dZ4umw19jI31dB6ityb1TbuiVyZ5R0NLn7ZdF2JPX73th83C2HXT-nRCjmqzSX7y62PyMr9-nt1m98ubu9n0PrMgsc_Q6go8FrVTVkgEa7Sz6FErUReYC84A8sqDdUxVAA4doDIOK41CcrQwJme7384kazZ1NK1tUtnF5sPEr5JrLTUOEMbkfNdLQ9SufCyrEN5TyVn5g7T8gxS-AcuoYTY</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Xie, Wen</creator><creator>Yu, Lean</creator><creator>Xu, Shanying</creator><creator>Wang, Shouyang</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>A New Method for Crude Oil Price Forecasting Based on Support Vector Machines</title><author>Xie, Wen ; Yu, Lean ; Xu, Shanying ; Wang, Shouyang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-4c9b3e47fd8c5643ca9dc4e4985f742510332be3cd08b33d4d348ad4b945614c3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>ARIMA Model</topic><topic>Computer science; control theory; systems</topic><topic>Data processing. List processing. Character string processing</topic><topic>Exact sciences and technology</topic><topic>Memory organisation. Data processing</topic><topic>Root Mean Square Error</topic><topic>Software</topic><topic>Support Vector Machine</topic><topic>Support Vector Machine Model</topic><topic>Support Vector Regression</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xie, Wen</creatorcontrib><creatorcontrib>Yu, Lean</creatorcontrib><creatorcontrib>Xu, Shanying</creatorcontrib><creatorcontrib>Wang, Shouyang</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xie, Wen</au><au>Yu, Lean</au><au>Xu, Shanying</au><au>Wang, Shouyang</au><au>Alexandrov, Vassil N.</au><au>Dongarra, Jack</au><au>van Albada, Geert Dick</au><au>Sloot, Peter M. A.</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>A New Method for Crude Oil Price Forecasting Based on Support Vector Machines</atitle><btitle>Computational Science – ICCS 2006</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><spage>444</spage><epage>451</epage><pages>444-451</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540343851</isbn><isbn>3540343857</isbn><isbn>3540343792</isbn><isbn>9783540343790</isbn><eisbn>9783540343868</eisbn><eisbn>3540343865</eisbn><abstract>This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The experiment results show that SVM outperforms the other two methods and is a fairly good candidate for the crude oil price prediction.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11758549_63</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Computational Science – ICCS 2006, 2006, p.444-451 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19969478 |
| source | Springer Books |
| subjects | Applied sciences ARIMA Model Computer science control theory systems Data processing. List processing. Character string processing Exact sciences and technology Memory organisation. Data processing Root Mean Square Error Software Support Vector Machine Support Vector Machine Model Support Vector Regression |
| title | A New Method for Crude Oil Price Forecasting Based on Support Vector Machines |
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