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Information entropic measures applied to multivariate fractional brownian motion

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dc.contributor.advisor SANTHANAM, M. S. en_US
dc.contributor.author ANAND, VIVEK en_US
dc.date.accessioned 2011-05-10T10:49:56Z
dc.date.available 2011-05-10T10:49:56Z
dc.date.issued 2011-05 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/137
dc.description.abstract Time series analysis gives us a window to look at the past events and make predictions about the future. It has been long since it was discovered that various natural process exhibit a long memory property, characterized by the Hurst parameter H. The main goal of this project is to extract significant information contained in large correlated multivariate time series in terms of information entropic measures. The data was projected onto principal components (using PCA) where maximum variance of the data was captured by information entropic measures. In this thesis we study the variation of the information entropy of the the top principal components (PCs) with a variation in H and find that as the value of H increases, the net information entropies of the top PCs decrease, indicating an increment in the amount of variation in top PCs as H increases. en_US
dc.language.iso en en_US
dc.subject 2011
dc.subject Long Memory en_US
dc.subject Time Series Analysis en_US
dc.title Information entropic measures applied to multivariate fractional brownian motion en_US
dc.type Thesis en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Mathematics en_US
dc.contributor.department Dept. of Physics en_US
dc.contributor.registration 20061039 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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