Interactive hidden Markov models and their applications

W. K. Ching, E. Fung, M. Ng, T. K. Siu, W. K. Li

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In this paper, we propose an Interactive hidden Markov model (IHMM). In a traditional 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. Numerical examples on the sales demand data and economic data are given to demonstrate the applicability of the model.

Original languageEnglish
Pages (from-to)85-97
Number of pages13
JournalIMA Journal of Management Mathematics
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 2007

Keywords

  • Categorical time series
  • Hidden Markov model
  • Prediction of demand
  • Steady-state probability distribution

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