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Coherent Forecasting for a Mixed Integer-Valued Time Series Model

Author

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  • Wooi Chen Khoo

    (Department of Applied Statistics, School of Mathematical Sciences, Sunway University, Subang Jaya 47500, Malaysia)

  • Seng Huat Ong

    (Institute of Actuarial Science and Data Analytics, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Biswas Atanu

    (Applied Statistics Unit, Indian Statistical Institute, 203 B.T Road, Kolkata 700108, India)

Abstract

In commerce, economics, engineering and the sciences, quantitative methods based on statistical models for forecasting are very useful tools for prediction and decision. There is an abundance of papers on forecasting for continuous-time series but relatively fewer papers for time series of counts which require special consideration due to the integer nature of the data. A popular method for modelling is the method of mixtures which is known for its flexibility and thus improved prediction capability. This paper studies the coherent forecasting for a flexible stationary mixture of Pegram and thinning (MPT) process, and develops the likelihood-based asymptotic distribution. Score functions and the Fisher information matrix are presented. Numerical studies are used to assess the performance of the forecasting methods. Also, a comparison is made with existing discrete-valued time series models. Finally, the practical application is illustrated with two sets of real data. It is shown that the mixture model provides good forecasting performance.

Suggested Citation

  • Wooi Chen Khoo & Seng Huat Ong & Biswas Atanu, 2022. "Coherent Forecasting for a Mixed Integer-Valued Time Series Model," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2961-:d:889814
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    References listed on IDEAS

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    Cited by:

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