  We investigate nonlinear regression for nonstationary sequential data. In
most real-life applications such as business domains including finance, retail,
energy and economy, timeseries data exhibits 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 vanilla RNN and conventional methods such as Markov Switching ARIMA 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.
