Interactive hidden Markov models and their applications”, IMA

In this paper, we propose an Interactive Hidden Markov Model (IHMM). In a tradi-tional HMM, the observable states are affected directly by the hidden states, but not vice versa. In the proposed IHMM, the transitions of hidden states depend on the observable states. We also develop an efficient estimation method for the model parameters. Numer-ical examples on the sales demand data and economic data are given to demonstrate the applicability of the model