Markovian RNN: An Adaptive Time Series Prediction Network With HMM-Based Switching for Nonstationary Environments

Abstract

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.

Assisting Healthcare Using a Time-Series-Based Nonstationary Model for Smart Beds

“…In Brandão and Porta Nova (2009), a technique that is built on traditional time-series approaches was suggested for evaluating continuous-time computer reactions under various nonstationary circumstances. There, methods for developing Meta models are presented, as are confidence ranges for nonstationary answers that are time-persistent, such as the quantity of entities in queues or the overall quantity stored in a system (Guo et al, 2022;Ilhan et al, 2023). These processes, however, did not apply to nonstationary time performance metrics, i.e.…”