Interactive hidden Markov models and their applications

W. K. Ching*, E. Fung, Kwok Po NG, T. K. Siu, W. K. Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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

Scopus Subject Areas

  • Management Information Systems
  • Modelling and Simulation
  • Economics, Econometrics and Finance(all)
  • Strategy and Management
  • Management Science and Operations Research
  • Applied Mathematics

User-Defined Keywords

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

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