A multivariate Markov chain model for categorical data sequences and its applications in demand predictions

Wai Ki Ching; Eric S. Fung; Michael K. Ng

doi:10.1093/imaman/13.3.187

A multivariate Markov chain model for categorical data sequences and its applications in demand predictions

Wai Ki Ching
, Eric S. Fung
, Michael K. Ng

Department of Mathematics

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

88 Citations (Scopus)

Abstract

Markov models are commonly used in modelling many practical systems such as telecommunication systems, manufacturing systems and inventory systems. In this paper we propose a multivariate Markov chain model for modelling multiple categorical data sequences. We develop efficient estimation methods for the model parameters. We then apply the model and method to demand predictions for a soft‐drink company in Hong Kong.

Original language	English
Pages (from-to)	187-199
Number of pages	13
Journal	IMA Journal of Management Mathematics
Volume	13
Issue number	3
DOIs	https://doi.org/10.1093/imaman/13.3.187
Publication status	Published - Jul 2002

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SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

Markov model
Multivariate
Demand prediction

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10.1093/imaman/13.3.187

Cite this

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title = "A multivariate Markov chain model for categorical data sequences and its applications in demand predictions",

abstract = "Markov models are commonly used in modelling many practical systems such as telecommunication systems, manufacturing systems and inventory systems. In this paper we propose a multivariate Markov chain model for modelling multiple categorical data sequences. We develop efficient estimation methods for the model parameters. We then apply the model and method to demand predictions for a soft‐drink company in Hong Kong.",

keywords = "Markov model, Multivariate, Demand prediction",

author = "Ching, \{Wai Ki\} and Fung, \{Eric S.\} and Ng, \{Michael K.\}",

note = "Publisher Copyright: {\textcopyright} The Institute of Mathematics and its Applications 2003. Funding Information: Research supported in part by RGC Grant No. HKU 7126/02P, 7132/00P and 7130/02P and HKU CRCG Grant Nos. 10203408, 10203501, 10203907, 10204436, 10203919.",

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T1 - A multivariate Markov chain model for categorical data sequences and its applications in demand predictions

AU - Ching, Wai Ki

AU - Fung, Eric S.

AU - Ng, Michael K.

N1 - Publisher Copyright: © The Institute of Mathematics and its Applications 2003. Funding Information: Research supported in part by RGC Grant No. HKU 7126/02P, 7132/00P and 7130/02P and HKU CRCG Grant Nos. 10203408, 10203501, 10203907, 10204436, 10203919.

PY - 2002/7

Y1 - 2002/7

N2 - Markov models are commonly used in modelling many practical systems such as telecommunication systems, manufacturing systems and inventory systems. In this paper we propose a multivariate Markov chain model for modelling multiple categorical data sequences. We develop efficient estimation methods for the model parameters. We then apply the model and method to demand predictions for a soft‐drink company in Hong Kong.

AB - Markov models are commonly used in modelling many practical systems such as telecommunication systems, manufacturing systems and inventory systems. In this paper we propose a multivariate Markov chain model for modelling multiple categorical data sequences. We develop efficient estimation methods for the model parameters. We then apply the model and method to demand predictions for a soft‐drink company in Hong Kong.

KW - Markov model

KW - Multivariate

KW - Demand prediction

UR - https://www.scopus.com/pages/publications/0345055705

U2 - 10.1093/imaman/13.3.187

DO - 10.1093/imaman/13.3.187

M3 - Review article

AN - SCOPUS:0345055705

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SP - 187

EP - 199

JO - IMA Journal of Management Mathematics

JF - IMA Journal of Management Mathematics

IS - 3

ER -

A multivariate Markov chain model for categorical data sequences and its applications in demand predictions

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