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A General Class of Holt-Winters Type Forecasting Models

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  • S. A. Roberts

    (Imperial Chemical Industries, Ltd., England)

Abstract

This paper is concerned with the formulation of short-term forecasting models, and introduces a range of models of considerable importance. These are defined in terms of predictions and sensible updating mechanisms for estimates of quantities such as level, growth, and seasonality, and constitute generalizations of familiar (linear) exponential smoothing predictors. They are shown to be equivalent to particular ARIMA models, and generally these do not lie within that subset of the ARIMA class which forms the basis of the Box-Jenkins modelling approach. It is argued that the models of this paper have a reasoned structure, and are to be preferred to the Box-Jenkins models for most socio-economic applications.

Suggested Citation

  • S. A. Roberts, 1982. "A General Class of Holt-Winters Type Forecasting Models," Management Science, INFORMS, vol. 28(7), pages 808-820, July.
    • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:7:p:808-820
    DOI: 10.1287/mnsc.28.7.808
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    Cited by:

    1. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    2. George Athanasopoulos & Ashton de Silva, 2010. "Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand," Monash Econometrics and Business Statistics Working Papers 11/09, Monash University, Department of Econometrics and Business Statistics.
    3. Li, Qinyun & Gaalman, Gerard & Disney, Stephen M., 2023. "On the equivalence of the proportional and damped trend order-up-to policies: An eigenvalue analysis," International Journal of Production Economics, Elsevier, vol. 265(C).
    4. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 407-426, June.
    5. Jiyoung Park & James E. Moore & Peter Gordon & Harry W. Richardson, 2017. "A New Approach to Quantifying the Impact of Hurricane-Disrupted Oil Refinery Operations Utilizing Secondary Data," Group Decision and Negotiation, Springer, vol. 26(6), pages 1125-1144, November.
    6. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
    7. Hao Chen & Ling He & Jiachuan Chen & Bo Yuan & Teng Huang & Qi Cui, 2019. "Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China," Sustainability, MDPI, vol. 11(22), pages 1-29, November.
    8. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
    9. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/01, Monash University, Department of Econometrics and Business Statistics.
    10. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    11. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    12. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
    13. Cote, Murray J., 2005. "A note on "Bed allocation techniques based on census data"," Socio-Economic Planning Sciences, Elsevier, vol. 39(2), pages 183-192, June.
    14. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    15. Chen, Chunhang, 1997. "Robustness properties of some forecasting methods for seasonal time series: A Monte Carlo study," International Journal of Forecasting, Elsevier, vol. 13(2), pages 269-280, June.
    16. Warburton, Roger D.H. & Hodgson, J.P.E. & Nielsen, E.H., 2014. "Exact solutions to the supply chain equations for arbitrary, time-dependent demands," International Journal of Production Economics, Elsevier, vol. 151(C), pages 195-205.
    17. Zhou, Shenghan & Hu, Chen & Qiao, Xiaoduo & Chang, Wenbing, 2016. "A forecasting method for Chinese civil planes attendance rate based on vague sets," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 518-526.
    18. Mustapha, Nazeem & Djolov, George, 2010. "The development and production of GDP flash estimates in a newly industrialised country: the case of South Africa," MPRA Paper 39215, University Library of Munich, Germany, revised 01 Dec 2010.

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    Keywords

    forecasting: time series;

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