Identification of Markov Jump Autoregressive Processes from Large Noisy Data Sets
For researchers in system identification, this work addresses the challenge of identifying switching dynamics under high noise, but the lack of concrete performance metrics and comparison to baselines makes the contribution incremental.
This paper presents a method for identifying Markov jump autoregressive processes from noisy data, handling high measurement noise by leveraging algebraic constraints and maximum likelihood estimation. The approach is shown to be effective when large datasets are available, though no specific numerical results are provided.
This paper introduces a novel methodology for the identification of switching dynamics for switched autoregressive linear models. Switching behavior is assumed to follow a Markov model. The system's outputs are contaminated by possibly large values of measurement noise. Although the procedure provided can handle other noise distributions, for simplicity, it is assumed that the distribution is Normal with unknown variance. Given noisy input-output data, we aim at identifying switched system coefficients, parameters of the noise distribution, dynamics of switching and probability transition matrix of Markovian model. System dynamics are estimated using previous results which exploit algebraic constraints that system trajectories have to satisfy. Switching dynamics are computed with solving a maximum likelihood estimation problem. The efficiency of proposed approach is shown with several academic examples. Although the noise to output ratio can be high, the method is shown to be extremely effective in the situations where a large number of measurements is available.