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

Area of Science:

• Machine Learning
• Time Series Analysis
• Deep Learning

Background:

• Real-world sequential data, common in finance, retail, and energy, often exhibits nonstationarity.
• Nonstationarity arises from evolving system dynamics, challenging traditional time series models.

Purpose of the Study:

• To develop a novel recurrent neural network (RNN) architecture for modeling nonstationary sequential data.
• To capture the adaptive, regime-switching dynamics inherent in many real-life time series.

Main Methods:

• Introduced a novel Markovian RNN architecture.
• Integrated a Hidden Markov Model (HMM) for adaptive regime transitions.
• Employed end-to-end joint optimization of the entire network.

Main Results:

• Achieved significant performance gains over conventional methods like Markov Switching ARIMA and other RNN variants.
• Demonstrated superior results on both synthetic and real-life datasets.
• Successfully interpreted model parameters and regime beliefs to analyze sequence dynamics.

Conclusions:

• The proposed Markovian RNN effectively models nonstationary sequential data by adaptively switching between regimes.
• This approach offers a powerful new tool for analyzing complex time series in various business domains.
• The model provides insights into the underlying dynamics driving temporal variations.