Interactive hidden Markov models and their applications |
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| Authors: | Ching, W. K. Fung, E. Ng, M. Siu, T. K. Li, W. K. |
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| Affiliation: | 1 The Advanced Modeling and Applied Computing Laboratory and Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, 2 Department of Mathematics, The Hong Kong Baptist University, Kowloon Tong, Hong Kong, 3 Department of Actuarial Mathematics and Statistics, School of Mathematical and Computer Sciences and the Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK, 4 Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong |
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| Abstract: | ** Email: wching{at}hkusua.hku.hk In this paper, we propose an Interactive hidden Markov model(IHMM). In a traditional HMM, the observable states are affecteddirectly by the hidden states, but not vice versa. In the proposedIHMM, the transitions of hidden states depend on the observablestates. We also develop an efficient estimation method for themodel parameters. Numerical examples on the sales demand dataand economic data are given to demonstrate the applicabilityof the model. |
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| Keywords: | hidden Markov model categorical time series steady-state probability distribution prediction of demand |
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