IDEAS home Printed from https://ideas.repec.org/a/ags/polpwa/329703.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/329703/files/artykul-2.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.329703?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    2. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    3. So, Mike K.P. & Chung, Ray S.W., 2014. "Dynamic seasonality in time series," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 212-226.
    4. Chethana Dharmawardane & Ville Sillanpää & Jan Holmström, 2021. "High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design," Operations Management Research, Springer, vol. 14(1), pages 38-60, June.
    5. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    6. Anis Chariri & Indira Januarti, 2017. "Audit Committee Characteristics and Integrated Reporting:Empirical Study of Companies Listed on the Johannesburg Stock Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 305-318.
    7. Zhanarys S. Raimbekov & Bakyt U. Syzdykbayeva & Kamshat P. Mussina & Luiza P. Moldashbayeva & Bakytzhamal A. Zhumataeva, 2017. "The Study of the Logistics Development Effectiveness in the Eurasian Economic Union Countries and Measures to Improve it," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 260-276.
    8. 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.
    9. Isabella Damdinovna Elyakova & Aleksandr Andreyevich Khristoforov & Aleksandr Lvovich Elyakov & Larisa Ivanovna Danilova & Tamara Aleksandrovna Karataeva & Elena Vladimirovna Danilova, 2017. "Forecast Scenarios of World Prices for Natural Gas," European Research Studies Journal, European Research Studies Journal, vol. 0(4A), pages 284-297.
    10. 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.
    11. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, 2002. "Forecasting for inventory control with exponential smoothing," International Journal of Forecasting, Elsevier, vol. 18(1), pages 5-18.
    12. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    13. Alexander Lvovich Elyakov & Isabella Damdinovna Elyakova & Larisa Ivanovna Danilova & Alexander Andreevich Khristoforov & Oleg Ilich Kondratev & Valentina Vasilyevna Grigoryeva, 2017. "History and Prospects of Natural Gas Pricing in Continental Europe in Conditions of Instability of World Oil Prices," European Research Studies Journal, European Research Studies Journal, vol. 0(4A), pages 323-338.
    14. Ferbar Tratar, Liljana, 2015. "Forecasting method for noisy demand," International Journal of Production Economics, Elsevier, vol. 161(C), pages 64-73.
    15. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    16. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics.
    17. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    18. 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.
    19. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    20. Rachidi, Ntebatše R. & Nwaila, Glen T. & Zhang, Steven E. & Bourdeau, Julie E. & Ghorbani, Yousef, 2021. "Assessing cobalt supply sustainability through production forecasting and implications for green energy policies," Resources Policy, Elsevier, vol. 74(C).

    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:ags:polpwa:329703. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/wesggpl.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.