Interactive hidden Markov models and their applications

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

doi:10.1093/imaman/dpl014

Interactive hidden Markov models and their applications

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

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Journal article › peer-review

9 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 language	English
Pages (from-to)	85-97
Number of pages	13
Journal	IMA Journal of Management Mathematics
Volume	18
Issue number	1
DOIs	https://doi.org/10.1093/imaman/dpl014
Publication status	Published - 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

Access to Document

10.1093/imaman/dpl014

Cite this

@article{37649e7d050149fba7594d66d6c94ff5,

title = "Interactive hidden Markov models and their applications",

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.",

keywords = "Categorical time series, Hidden Markov model, Prediction of demand, Steady-state probability distribution",

author = "Ching, {W. K.} and E. Fung and M. Ng and Siu, {T. K.} and Li, {W. K.}",

note = "Funding Information: Research was supported in part by HKU CRCG Grants and Strategic Research Theme Fund on Computational Physics and Numerical Methods, RGC 7046/03P, 7035/04P, 7035/05P and HKBU FRGs.",

year = "2007",

month = jan,

doi = "10.1093/imaman/dpl014",

language = "English",

volume = "18",

pages = "85--97",

journal = "IMA Journal of Management Mathematics",

issn = "1471-678X",

publisher = "Oxford University Press",

number = "1",

}

TY - JOUR

T1 - Interactive hidden Markov models and their applications

AU - Ching, W. K.

AU - Fung, E.

AU - Ng, M.

AU - Siu, T. K.

AU - Li, W. K.

N1 - Funding Information: Research was supported in part by HKU CRCG Grants and Strategic Research Theme Fund on Computational Physics and Numerical Methods, RGC 7046/03P, 7035/04P, 7035/05P and HKBU FRGs.

PY - 2007/1

Y1 - 2007/1

N2 - 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.

AB - 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.

KW - Categorical time series

KW - Hidden Markov model

KW - Prediction of demand

KW - Steady-state probability distribution

UR - http://www.scopus.com/inward/record.url?scp=33845383467&partnerID=8YFLogxK

U2 - 10.1093/imaman/dpl014

DO - 10.1093/imaman/dpl014

M3 - Journal article

AN - SCOPUS:33845383467

SN - 1471-678X

VL - 18

SP - 85

EP - 97

JO - IMA Journal of Management Mathematics

JF - IMA Journal of Management Mathematics

IS - 1

ER -

Interactive hidden Markov models and their applications

Abstract

Scopus Subject Areas

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this