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Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data

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  • Pleños, Mary Cris F.

Abstract

This study utilized the data on abaca fiber production and used Holt-Winters model to forecast the abaca fiber production since the studied variable is characterized by a fairly strong intensity of seasonality. For the construction of forecasts, additive and multiplicative models were used. The most accurate forecasts were selected on the basis of Mean Square Error, Root Mean Square Error, Mean Absolute Percentage Error, and Mean Absolute Scaled Error. It was found that the multiplicative method had a higher accuracy, hence it was utilized to forecast the production for the next three years. According to the findings, the anticipated fiber production for 2021-2023 showed an increase up to the second quarter, but then declining afterwards.

Suggested Citation

  • Pleños, Mary Cris F., 2022. "Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 22(2), June.
    • Handle: RePEc:ags:polpwa:329703
    DOI: 10.22004/ag.econ.329703
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    References listed on IDEAS

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    1. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286.
    2. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    3. Emrah Gecili & Assem Ziady & Rhonda D Szczesniak, 2021. "Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-11, January.
    4. Paul Goodwin, 2010. "The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 19, pages 30-33, Fall.
    Full references (including those not matched with items on IDEAS)

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