1 Ocak 2021 Dergi makalesi Açık Erişim

Ilhan, Fatih; Karaahmetoglu, Oguzhan; Balaban, Ismail; Kozat, Suleyman Serdar

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/234290</identifier>
  <creators>
    <creator>
      <creatorName>Ilhan, Fatih</creatorName>
      <givenName>Fatih</givenName>
      <familyName>Ilhan</familyName>
    </creator>
    <creator>
      <creatorName>Karaahmetoglu, Oguzhan</creatorName>
      <givenName>Oguzhan</givenName>
      <familyName>Karaahmetoglu</familyName>
    </creator>
    <creator>
      <creatorName>Balaban, Ismail</creatorName>
      <givenName>Ismail</givenName>
      <familyName>Balaban</familyName>
      <affiliation>DataBoss AS, ODTU Teknokent, TR-06800 Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kozat, Suleyman Serdar</creatorName>
      <givenName>Suleyman Serdar</givenName>
      <familyName>Kozat</familyName>
    </creator>
  </creators>
  <titles>
    <title>Markovian Rnn: An Adaptive Time Series Prediction Network With Hmm-Based Switching For Nonstationary Environments</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/234290</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TNNLS.2021.3100528</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">We investigate nonlinear regression for nonstationary sequential data. In most real-life applications such as business domains including finance, retail, energy, and economy, time series data exhibit nonstationarity due to the temporally varying dynamics of the underlying system. 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 independently. 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.</description>
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