IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v28y1982i9p1035-1044.html
   My bibliography  Save this article

Forecasting Applications of an Adaptive Multiple Exponential Smoothing Model

Author

Listed:
  • Phillip G. Enns

    (St. Louis University)

  • Joseph A. Machak

    (University of Michigan)

  • W. Allen Spivey

    (University of Michigan)

  • William J. Wrobleski

    (University of Michigan)

Abstract

This paper introduces a class of multiple exponential smoothing models useful in automated or minimal intervention industrial forecasting systems. These models are an alternative to simple univariate exponential smoothing and Trigg and Leach type adaptive models, which treat time series as unrelated and so cannot explicitly accommodate interrelationships that may exist between two or more time series. Moreover, the multiple models are adaptive in that the smoothing matrix, which is a generalization of the smoothing constant of univariate models, changes from period to period. Maximum likelihood estimates of the model parameters, including the full variance-covariance structure as well as the smoothing matrix, are provided, thus freeing the model user from the need for making ad hoc estimates of parameter values, a feature of simple univariate exponential smoothing. The forecast performance of this multiple time series model is compared with that of other univariate models using automobile sales data and some promising results are obtained.

Suggested Citation

  • Phillip G. Enns & Joseph A. Machak & W. Allen Spivey & William J. Wrobleski, 1982. "Forecasting Applications of an Adaptive Multiple Exponential Smoothing Model," Management Science, INFORMS, vol. 28(9), pages 1035-1044, September.
  • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:9:p:1035-1044
    DOI: 10.1287/mnsc.28.9.1035
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.28.9.1035
    Download Restriction: no

    Citations

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


    Cited by:

    1. 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.
    2. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.
    3. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
    4. Bermúdez, José D. & Corberán-Vallet, Ana & Vercher, Enriqueta, 2009. "Multivariate exponential smoothing: A Bayesian forecast approach based on simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1761-1769.
    5. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    6. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
    7. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    8. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.

    More about this item

    Keywords

    forecasting: time series;

    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:inm:ormnsc:v:28:y:1982:i:9:p:1035-1044. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Matthew Walls). General contact details of provider: http://edirc.repec.org/data/inforea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.