1 Ocak 2021 Dergi makalesi Açık Erişim

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

Dublin Core

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  <dc:creator>Ilhan, Fatih</dc:creator>
  <dc:creator>Karaahmetoglu, Oguzhan</dc:creator>
  <dc:creator>Balaban, Ismail</dc:creator>
  <dc:creator>Kozat, Suleyman Serdar</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/234290</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:234290</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS</dc:source>
  <dc:title>Markovian RNN: An Adaptive Time Series Prediction Network With HMM-Based Switching for Nonstationary Environments</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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