Markov anahtarlamalı tekrarlayan yapay sinir ağları ile durağan olmayan zaman serisi tahmini

We investigate nonlinear prediction for nonstationary time series. In most real-life scenarios such as finance, retail, energy and economy applications, time series 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 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.