Financial Prediction Using Manifold Wavelet Kernel
This paper constructs an admissible manifold wavelet kernel (MWK) for support vector machine (SVM) to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity (GARCH) model. The MWK is obtained by incorporating the wavelet technique and mani...
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| creator | Ling-Bing Tang HuanYe Sheng |
| description | This paper constructs an admissible manifold wavelet kernel (MWK) for support vector machine (SVM) to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity (GARCH) model. The MWK is obtained by incorporating the wavelet technique and manifold theory into SVM. Unlike Gaussian kernel in SVM, the MWK can approximate arbitrary nonlinear functions. The applicability and validity of MWK for volatility forecast are confirmed through experiments on simulated data sets. |
| doi_str_mv | 10.1109/WMWA.2009.77 |
| format | Conference Proceeding |
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The MWK is obtained by incorporating the wavelet technique and manifold theory into SVM. Unlike Gaussian kernel in SVM, the MWK can approximate arbitrary nonlinear functions. 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The MWK is obtained by incorporating the wavelet technique and manifold theory into SVM. Unlike Gaussian kernel in SVM, the MWK can approximate arbitrary nonlinear functions. The applicability and validity of MWK for volatility forecast are confirmed through experiments on simulated data sets.</description><subject>Computational modeling</subject><subject>Computer science</subject><subject>Educational institutions</subject><subject>GARCH forecast</subject><subject>Kernel</subject><subject>Manifolds</subject><subject>MWK</subject><subject>Polynomials</subject><subject>Predictive models</subject><subject>Risk management</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><isbn>9780769536460</isbn><isbn>0769536468</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjU1Lw0AURQekoNbs3LnJH0h98_1mWYrVYosuLFmWl-SNjMSpJEHw39uiqwv3cM8V4lbCQkoI9_WuXi4UQFh4fyGK4BG8C1Y742Amrs8kgEY0l6IYxw8AkMF568yVUOuUKbeJ-vJ14C61Uzrmcj-m_F7uKKd47Luypm_ueSqfecjc34hZpH7k4j_nYr9-eFs9VduXx81qua2S9HaquHWu0dEhq_M1WeMjREKFDTLLYLylNlrjVIsarDJ0mmHXGD6VKEnPxd2fNzHz4WtInzT8HKzSyjjUvwHtQ7k</recordid><startdate>200906</startdate><enddate>200906</enddate><creator>Ling-Bing Tang</creator><creator>HuanYe Sheng</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200906</creationdate><title>Financial Prediction Using Manifold Wavelet Kernel</title><author>Ling-Bing Tang ; HuanYe Sheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-ec66b3f68e23884a547f0fa828b8ee19475acf5462c830524a1758db4ef5481a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Computational modeling</topic><topic>Computer science</topic><topic>Educational institutions</topic><topic>GARCH forecast</topic><topic>Kernel</topic><topic>Manifolds</topic><topic>MWK</topic><topic>Polynomials</topic><topic>Predictive models</topic><topic>Risk management</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Ling-Bing Tang</creatorcontrib><creatorcontrib>HuanYe Sheng</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ling-Bing Tang</au><au>HuanYe Sheng</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Financial Prediction Using Manifold Wavelet Kernel</atitle><btitle>2009 Second Pacific-Asia Conference on Web Mining and Web-based Application</btitle><stitle>WMWA</stitle><date>2009-06</date><risdate>2009</risdate><spage>63</spage><epage>65</epage><pages>63-65</pages><isbn>9780769536460</isbn><isbn>0769536468</isbn><abstract>This paper constructs an admissible manifold wavelet kernel (MWK) for support vector machine (SVM) to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity (GARCH) model. The MWK is obtained by incorporating the wavelet technique and manifold theory into SVM. Unlike Gaussian kernel in SVM, the MWK can approximate arbitrary nonlinear functions. The applicability and validity of MWK for volatility forecast are confirmed through experiments on simulated data sets.</abstract><pub>IEEE</pub><doi>10.1109/WMWA.2009.77</doi><tpages>3</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computational modeling Computer science Educational institutions GARCH forecast Kernel Manifolds MWK Polynomials Predictive models Risk management Support vector machine classification Support vector machines |
| title | Financial Prediction Using Manifold Wavelet Kernel |
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