Nonstationary time series prediction with Markovian switching recurrent neural networks
buir.advisor | Kozat, Süleyman Serdar | |
dc.contributor.author | İlhan, Fatih | |
dc.date.accessioned | 2021-08-17T11:14:25Z | |
dc.date.available | 2021-08-17T11:14:25Z | |
dc.date.copyright | 2021-07 | |
dc.date.issued | 2021-07 | |
dc.date.submitted | 2021-08-06 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Includes bibliographical references (leaves 42-46). | en_US |
dc.description.abstract | We 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.statementofresponsibility | by Fatih İlhan | en_US |
dc.format.extent | x, 46 leaves : illustrations, charts ; 30 cm. | en_US |
dc.identifier.itemid | B138604 | |
dc.identifier.uri | http://hdl.handle.net/11693/76447 | |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Time series prediction | en_US |
dc.subject | Recurrent neural networks | en_US |
dc.subject | Nonstationarity | en_US |
dc.subject | Regime switching | en_US |
dc.subject | Nonlinear regression | en_US |
dc.subject | Hidden Markov models | en_US |
dc.title | Nonstationary time series prediction with Markovian switching recurrent neural networks | en_US |
dc.title.alternative | Markov anahtarlamalı tekrarlayan yapay sinir ağları ile durağan olmayan zaman serisi tahmini | en_US |
dc.type | Thesis | en_US |
thesis.degree.discipline | Electrical and Electronic Engineering | |
thesis.degree.grantor | Bilkent University | |
thesis.degree.level | Master's | |
thesis.degree.name | MS (Master of Science) |