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Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
[article]
2020
arXiv
pre-print
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
arXiv:2006.10119v1
fatcat:4calty7qrffnvpftwzlpe36bj4