Sequential prediction over hierarchical structures

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dc.citation.epage6298en_US
dc.citation.issueNumber23en_US
dc.citation.spage6284en_US
dc.citation.volumeNumber64en_US
dc.contributor.authorVanli, N. D.en_US
dc.contributor.authorGokcesu, K.en_US
dc.contributor.authorSayin, M. O.en_US
dc.contributor.authorYildiz, H.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2018-04-12T10:42:16Z
dc.date.available2018-04-12T10:42:16Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe study sequential compound decision problems in the context of sequential prediction of real valued sequences. In particular, we consider finite state (FS) predictors that are constructed based on a hierarchical structure, such as the order preserving patterns of the sequence history. We define hierarchical equivalence classes by tying certain models at a hierarchy level in a recursive manner in order to mitigate undertraining problems. These equivalence classes defined on a hierarchical structure are then used to construct a super exponential number of sequential FS predictors based on their combinations and permutations. We then introduce truly sequential algorithms with computational complexity only linear in the pattern length that 1) asymptotically achieve the performance of the best FS predictor or the best linear combination of all the FS predictors in an individual sequence manner without any stochastic assumptions over any data length n under a wide range of loss functions; 2) achieve the mean square error of the best linear combination of all FS filters or predictors in the steady-state for certain nonstationary models. We illustrate the superior convergence and tracking capabilities of our algorithm with respect to several state-of-the-art methods in the literature through simulations over synthetic and real benchmark data. © 1991-2012 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:42:16Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1109/TSP.2016.2607141en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/36494
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSP.2016.2607141en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectBig dataen_US
dc.subjectFinite state machineen_US
dc.subjectHierarchical modelingen_US
dc.subjectOnline learningen_US
dc.subjectSequential predictionen_US
dc.titleSequential prediction over hierarchical structuresen_US
dc.typeArticleen_US

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