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Improving prediction of exchange rates using Differential EMD
► The algorithm of Differential EMD has the capability of smoothing and reducing the noise. ► The SVR model can predict exchange rates. ► In prediction, the Differential EMD and SVR model outperforms the MS-GARCH model. Volatility is a key parameter when measuring the size of errors made in modellin...
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Published in: | Expert systems with applications 2013-01, Vol.40 (1), p.377-384 |
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description | ► The algorithm of Differential EMD has the capability of smoothing and reducing the noise. ► The SVR model can predict exchange rates. ► In prediction, the Differential EMD and SVR model outperforms the MS-GARCH model.
Volatility is a key parameter when measuring the size of errors made in modelling returns and other financial variables such as exchanged rates. The autoregressive moving-average (ARMA) model is a linear process in time series; whilst in the nonlinear system, the generalised autoregressive conditional heteroskedasticity (GARCH) and Markov switching GARCH (MS-GARCH) have been widely applied. In statistical learning theory, support vector regression (SVR) plays an important role in predicting nonlinear and nonstationary time series variables. In this paper, we propose a new algorithm, differential Empirical Mode Decomposition (EMD) for improving prediction of exchange rates under support vector regression (SVR). The new algorithm of Differential EMD has the capability of smoothing and reducing the noise, whereas the SVR model with the filtered dataset improves predicting the exchange rates. Simulations results consisting of the Differential EMD and SVR model show that our model outperforms simulations by a state-of-the-art MS-GARCH and Markov switching regression (MSR) models. |
doi_str_mv | 10.1016/j.eswa.2012.07.048 |
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Volatility is a key parameter when measuring the size of errors made in modelling returns and other financial variables such as exchanged rates. The autoregressive moving-average (ARMA) model is a linear process in time series; whilst in the nonlinear system, the generalised autoregressive conditional heteroskedasticity (GARCH) and Markov switching GARCH (MS-GARCH) have been widely applied. In statistical learning theory, support vector regression (SVR) plays an important role in predicting nonlinear and nonstationary time series variables. In this paper, we propose a new algorithm, differential Empirical Mode Decomposition (EMD) for improving prediction of exchange rates under support vector regression (SVR). The new algorithm of Differential EMD has the capability of smoothing and reducing the noise, whereas the SVR model with the filtered dataset improves predicting the exchange rates. Simulations results consisting of the Differential EMD and SVR model show that our model outperforms simulations by a state-of-the-art MS-GARCH and Markov switching regression (MSR) models.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2012.07.048</identifier><language>eng</language><publisher>Amsterdam: Elsevier Ltd</publisher><subject>Algorithms ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Computer simulation ; Empirical Mode Decomposition ; Exact sciences and technology ; Exchange rates ; Inference from stochastic processes; time series analysis ; Linear inference, regression ; Markov processes ; Markov switching GARCH ; Markov switching regression ; Mathematical analysis ; Mathematical models ; Mathematics ; Operational research and scientific management ; Operational research. Management science ; Portfolio theory ; Prediction ; Probability and statistics ; Regression ; Sciences and techniques of general use ; Statistics ; Support vector regression ; Time series ; Vectors (mathematics)</subject><ispartof>Expert systems with applications, 2013-01, Vol.40 (1), p.377-384</ispartof><rights>2012 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-b32ee95b957b2d04cff7b8d196a08b6b554dbfed64d9f90e10d5ffa3e69666ac3</citedby><cites>FETCH-LOGICAL-c396t-b32ee95b957b2d04cff7b8d196a08b6b554dbfed64d9f90e10d5ffa3e69666ac3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27095915$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Premanode, Bhusana</creatorcontrib><creatorcontrib>Toumazou, Chris</creatorcontrib><title>Improving prediction of exchange rates using Differential EMD</title><title>Expert systems with applications</title><description>► The algorithm of Differential EMD has the capability of smoothing and reducing the noise. ► The SVR model can predict exchange rates. ► In prediction, the Differential EMD and SVR model outperforms the MS-GARCH model.
Volatility is a key parameter when measuring the size of errors made in modelling returns and other financial variables such as exchanged rates. The autoregressive moving-average (ARMA) model is a linear process in time series; whilst in the nonlinear system, the generalised autoregressive conditional heteroskedasticity (GARCH) and Markov switching GARCH (MS-GARCH) have been widely applied. In statistical learning theory, support vector regression (SVR) plays an important role in predicting nonlinear and nonstationary time series variables. In this paper, we propose a new algorithm, differential Empirical Mode Decomposition (EMD) for improving prediction of exchange rates under support vector regression (SVR). The new algorithm of Differential EMD has the capability of smoothing and reducing the noise, whereas the SVR model with the filtered dataset improves predicting the exchange rates. Simulations results consisting of the Differential EMD and SVR model show that our model outperforms simulations by a state-of-the-art MS-GARCH and Markov switching regression (MSR) models.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer simulation</subject><subject>Empirical Mode Decomposition</subject><subject>Exact sciences and technology</subject><subject>Exchange rates</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Linear inference, regression</subject><subject>Markov processes</subject><subject>Markov switching GARCH</subject><subject>Markov switching regression</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Portfolio theory</subject><subject>Prediction</subject><subject>Probability and statistics</subject><subject>Regression</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><subject>Support vector regression</subject><subject>Time series</subject><subject>Vectors (mathematics)</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkD1PwzAQhi0EEqXwB5iyILEknBPHjiUYUPmqVMQCs-U4Z3CVJsVOC_x7HBUxwnTLc--99xBySiGjQPnFMsPwobMcaJ6ByIBVe2RCK1GkXMhin0xAliJlVLBDchTCEoAKADEhV_PV2vdb170ma4-NM4Pru6S3CX6aN929YuL1gCHZhBG5cdaix25wuk1uH2-OyYHVbcCTnzklL3e3z7OHdPF0P59dL1JTSD6kdZEjyrKOHeq8AWasFXXVUMk1VDWvy5I1tcWGs0ZaCUihKa3VBXLJOdemmJLzXW7s-r7BMKiVCwbbVnfYb4KKz1AoKK_y_9GCSSZyycqI5jvU-D4Ej1atvVtp_6UoqFGrWqpRqxq1KhAqao1LZz_5OhjdWq8748LvZi6iaUnH8Msdh9HL1qFXwTjsTHTs0Qyq6d1fZ74BnTKNkg</recordid><startdate>201301</startdate><enddate>201301</enddate><creator>Premanode, Bhusana</creator><creator>Toumazou, Chris</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201301</creationdate><title>Improving prediction of exchange rates using Differential EMD</title><author>Premanode, Bhusana ; Toumazou, Chris</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-b32ee95b957b2d04cff7b8d196a08b6b554dbfed64d9f90e10d5ffa3e69666ac3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Computer simulation</topic><topic>Empirical Mode Decomposition</topic><topic>Exact sciences and technology</topic><topic>Exchange rates</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Linear inference, regression</topic><topic>Markov processes</topic><topic>Markov switching GARCH</topic><topic>Markov switching regression</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Portfolio theory</topic><topic>Prediction</topic><topic>Probability and statistics</topic><topic>Regression</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><topic>Support vector regression</topic><topic>Time series</topic><topic>Vectors (mathematics)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Premanode, Bhusana</creatorcontrib><creatorcontrib>Toumazou, Chris</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Premanode, Bhusana</au><au>Toumazou, Chris</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving prediction of exchange rates using Differential EMD</atitle><jtitle>Expert systems with applications</jtitle><date>2013-01</date><risdate>2013</risdate><volume>40</volume><issue>1</issue><spage>377</spage><epage>384</epage><pages>377-384</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>► The algorithm of Differential EMD has the capability of smoothing and reducing the noise. ► The SVR model can predict exchange rates. ► In prediction, the Differential EMD and SVR model outperforms the MS-GARCH model.
Volatility is a key parameter when measuring the size of errors made in modelling returns and other financial variables such as exchanged rates. The autoregressive moving-average (ARMA) model is a linear process in time series; whilst in the nonlinear system, the generalised autoregressive conditional heteroskedasticity (GARCH) and Markov switching GARCH (MS-GARCH) have been widely applied. In statistical learning theory, support vector regression (SVR) plays an important role in predicting nonlinear and nonstationary time series variables. In this paper, we propose a new algorithm, differential Empirical Mode Decomposition (EMD) for improving prediction of exchange rates under support vector regression (SVR). The new algorithm of Differential EMD has the capability of smoothing and reducing the noise, whereas the SVR model with the filtered dataset improves predicting the exchange rates. Simulations results consisting of the Differential EMD and SVR model show that our model outperforms simulations by a state-of-the-art MS-GARCH and Markov switching regression (MSR) models.</abstract><cop>Amsterdam</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2012.07.048</doi><tpages>8</tpages></addata></record> |
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subjects | Algorithms Applied sciences Artificial intelligence Computer science control theory systems Computer simulation Empirical Mode Decomposition Exact sciences and technology Exchange rates Inference from stochastic processes time series analysis Linear inference, regression Markov processes Markov switching GARCH Markov switching regression Mathematical analysis Mathematical models Mathematics Operational research and scientific management Operational research. Management science Portfolio theory Prediction Probability and statistics Regression Sciences and techniques of general use Statistics Support vector regression Time series Vectors (mathematics) |
title | Improving prediction of exchange rates using Differential EMD |
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