IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v27y1978i3p264-279.html

The Holt‐Winters Forecasting Procedure

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2347162
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2347162?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Bożena Gajdzik & Radosław Wolniak & Anna Sączewska-Piotrowska & Wiesław Wes Grebski, 2025. "Polish Steel Production Under Conditions of Decarbonization—Steel Volume Forecasts Using Time Series and Multiple Linear Regression," Energies, MDPI, vol. 18(7), pages 1-38, March.
    3. Bufalo, Michele & Fanelli, Viviana, 2025. "A seasonal two-factor model for solar energy production: A climate extreme events analysis," Energy Economics, Elsevier, vol. 148(C).
    4. Vladislav Bína, Jitka Bartosová, Vladimir Pribyl, 2022. "Anomaly Detection in Time Series for Smart Agriculture," International Journal of Management, Knowledge and Learning, ToKnowPress, vol. 11, pages 177-186.
    5. 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.
    6. 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.
    7. Degiannakis, Stavros & Kafousaki, Eleftheria, 2025. "Disaggregating VIX," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1559-1588.
    8. Sultana, Nifat & Tsutsumida, Narumasa, 2025. "A review on data-driven methods for solar energy forecasting," Applied Energy, Elsevier, vol. 400(C).
    9. 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.
    10. 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.
    11. 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.
    12. Lilin Fan & Zhaoyu Song & Wentao Mao & Tiejun Luo & Wanting Wang & Kai Yang & Fukang Cao, 2025. "Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 3983-4003, August.
    13. S. R. Luwang & A. Rai & Md. Nurujjaman & F. Petroni, 2025. "Intraday order transition dynamics in high, medium, and low market cap stocks: A Markov chain approach," Papers 2502.07625, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:27:y:1978:i:3:p:264-279. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.