Nonstationary time series prediction with Markovian switching recurrent neural networks

buir.advisorKozat, Süleyman Serdar
dc.contributor.authorİlhan, Fatih
dc.date.accessioned2021-08-17T11:14:25Z
dc.date.available2021-08-17T11:14:25Z
dc.date.copyright2021-07
dc.date.issued2021-07
dc.date.submitted2021-08-06
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionIncludes bibliographical references (leaves 42-46).en_US
dc.description.abstractWe investigate nonlinear prediction for nonstationary time series. In most real-life scenarios such as finance, retail, energy and economy applications, time se-ries data exhibits nonstationarity due to the temporally varying dynamics of the underlying system. This situation makes the time series prediction challenging in nonstationary environments. We introduce a novel recurrent neural network (RNN) architecture, which adaptively switches between internal regimes in a Markovian way to model the nonstationary nature of the given data. Our model, Markovian RNN employs a hidden Markov model (HMM) for regime transitions, where each regime controls hidden state transitions of the recurrent cell inde-pendently. We jointly optimize the whole network in an end-to-end fashion. We demonstrate the significant performance gains compared to conventional methods such as Markov Switching ARIMA, RNN variants and recent statistical and deep learning-based methods through an extensive set of experiments with synthetic and real-life datasets. We also interpret the inferred parameters and regime belief values to analyze the underlying dynamics of the given sequences.en_US
dc.description.statementofresponsibilityby Fatih İlhanen_US
dc.format.extentx, 46 leaves : illustrations, charts ; 30 cm.en_US
dc.identifier.itemidB138604
dc.identifier.urihttp://hdl.handle.net/11693/76447
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTime series predictionen_US
dc.subjectRecurrent neural networksen_US
dc.subjectNonstationarityen_US
dc.subjectRegime switchingen_US
dc.subjectNonlinear regressionen_US
dc.subjectHidden Markov modelsen_US
dc.titleNonstationary time series prediction with Markovian switching recurrent neural networksen_US
dc.title.alternativeMarkov anahtarlamalı tekrarlayan yapay sinir ağları ile durağan olmayan zaman serisi tahminien_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical and Electronic Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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