Implementation of Using HMM-GA In Time Series Data

Some time series modeling methods have weaknesses, the static and dynamic information can not be consistently combined. Hidden Markov Model provides solutions to these problems. Hidden Markov Model (HUM) is an extension of the Markov chain where the state cannot be observed directly (hidden), but can only be observed through another set of observations. One of the problems in HMM is how to maximizing P(O vertical bar lambda)where O is an observation and lambda is a model parameter consists of transition matrices, emission matrices, and initial opportunity vectors which can be solved by the Baum-Welch algorithm. In practice, the Baum-Welch algorithm produces a model that is not optimal because this algorithm is very dependent on determining the initial parameters. To solve these problems, HMM will be combined with genetic algorithms (Hybrid GA-HMM). In general, based on AIC and BIC value, Hybrid GA-HMM is optimal than HMM. (C) 2021 The Authors. Published by Elsevier B.V.