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The Holt‐Winters Forecasting Procedure

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  • C. Chatfield

Abstract

The Holt‐Winters forecasting procedure is a simple widely used projection method which can cope with trend and seasonal variation. However, empirical studies have tended to show that the method is not as accurate on average as the more complicated Box‐Jenkins procedure. This paper points out that these empirical studies have used the automatic version of the method, whereas a non‐automatic version is also possible in which subjective judgement is employed, for example, to choose the correct model for seasonality. The paper re‐analyses seven series from the Newbold‐Granger study for which Box‐Jenkins forecasts were reported to be much superior to the (automatic) Holt‐Winters forecasts. The series do not appear to have any common properties, but it is shown that the automatic Holt‐Winters forecasts can often be improved by subjective modifications. It is argued that a fairer comparison would be that between Box‐Jenkins and a non‐automatic version of Holt‐Winters. Some general recommendations are made concerning the choice of a univariate forecasting procedure. The paper also makes suggestions regarding the implementation of the Holt‐Winters procedure, including a choice of starting values.

Suggested Citation

  • C. Chatfield, 1978. "The Holt‐Winters Forecasting Procedure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(3), pages 264-279, November.
    • Handle: RePEc:bla:jorssc:v:27:y:1978:i:3:p:264-279
    DOI: 10.2307/2347162
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    Cited by:

    1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Rafael Sánchez-Durán & Julio Barbancho & Joaquín Luque, 2019. "Solar Energy Production for a Decarbonization Scenario in Spain," Sustainability, MDPI, vol. 11(24), pages 1-29, December.
    3. Francesco Addabbo & Massimo Giotta & Antonia Mincuzzi & Aldo Sante Minerba & Rosa Prato & Francesca Fortunato & Nicola Bartolomeo & Paolo Trerotoli, 2023. "No Excess of Mortality from Lung Cancer during the COVID-19 Pandemic in an Area at Environmental Risk: Results of an Explorative Analysis," IJERPH, MDPI, vol. 20(8), pages 1-16, April.
    4. Isra Al-Turaiki & Fahad Almutlaq & Hend Alrasheed & Norah Alballa, 2021. "Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia," IJERPH, MDPI, vol. 18(16), pages 1-19, August.
    5. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
    6. Rameshwar Garg & Shriya Barpanda & Girish Rao Salanke N S & Ramya S, 2022. "Machine Learning Algorithms for Time Series Analysis and Forecasting," Papers 2211.14387, arXiv.org.

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