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

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Mary Pleños

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.

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Pleños, M. (2022). Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data. Zeszyty Naukowe SGGW W Warszawie - Problemy Rolnictwa Światowego, 22(2), 17–29. https://doi.org/10.22630/PRS.2022.22.2.6

Australian Bureau of Statistics (2022). Retrieved from https://www.abs.gov.au/websitedbs/d3310114.nsf/home/time+series+analysis:+the+basics.

Alharbi, N. (2021). Forecasting the COVID-19 Pandemic in Saudi Arabia Using a Modified Singular Spectrum Analysis Approach: Model Development and Data Analysis.

Analytics Vidhya (2022). Retrieved from https://www.analyticsvidhya.com/blog/2021/08/holt-winters-method-for-time-series-analysis/ Copyright 2013.

Food and Agriculture Organization (2021). Retrieved from https://www.fao.org/economic/futurefibres/ fibers/abaca0/en/.

Far Eastern Agriculture (2021). Retrieved from https://www.fareasternagriculture.com/crops/agriculture/abaca-the-philippine-fiber.

Gecili, E., Ziady, A., Szczesniak, R.D. (2021). Forecasting COVID-19 confirmed cases, deaths, recoveries: Revisiting established time series modeling through novel application for the USA and Italy.

Goodwin, P. (2010). The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong. Retrieved from www.forecasters.org/foresight.

Gundalia, M., Dholakia M.B. (2012). Prediction of maximum/minimum temperatures using Holt-Winters Method with Excel Spread Sheet for Junagadh Region. International Journal of Engineering Research & Technology, 1(6).

Hyndman, R.J., Athanasopoulos, G. (2021). Holt-Winters’ seasonal method. Forecasting: principals and practice, 3rd Ed. Retrieved from https://otexts.com/fpp3/holt-winters.html.

Koehler, A.B., Snyder, R.D., Keith, J. (1999). Forecasting Models and Prediction Intervals for the Multiplicative Holt-Winters Method. Department of Econometrics and Business Statistics Monash University

Kuzmin, A.G., Bykov, V.M., Kazaryan, M.A., Danko, T.P., Sekerin, V.D. (2017). Employing the Holt–Winters Model to Forecast and Assess the Efficiency of the Methods Used to Plan a Firm’s Sales in the Upmarket Sector. International Journal of Economic Research Vol 14 (7).

Lima, S., Goncalves, M., Costa, M. (2019). Time Series forecasting using Holt-Winters Exponential Smoothing: An Application to Economic Data. https;//doi.org/10.1063/1.5137999.

Liu, Z., Guo, W. (2020). Government Responses Matter: Predicting COVID-19 cases in US using an empirical Bayesian time series framework.

Mgale, Y.J., Yan, Y.X., Timothy, S. (2021). A Comparative Study of ARIMA and Holt-Winters Exponential Smoothing Models for Rice Price Forecasting in Tanzania. Open Access Library Journal, 8, 1-9. DOI: 10.4236/oalib.1107381.

Nusyirwan, N., Ahmad Faisol, F. (2020). Forecasting Seasonal Time Series Data using The Holt-Winters Exponential Smoothing Method of Additive Models. Jurnal Matematika Integratif 16(2), 151-157. doi:10.24198/jmi.v16.n2.29293.151-157

Panda, M. (2020). Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID 19 for India and its states. https://doi.org/10.1101/2020.07.14.20153908.

Philippines General Consulate - Vancouver, Canada (2020). Retrieved from https://www.vancouverpcg.org/resources-listofrestaurants/yamang-pinoy/abaca/the-philippine-abacaindustry.

Philippine Statistics Authority (2021). Retrieved from https://psa.gov.ph/non-food.

Pelegrini, F.R., Fogliatto, F. (2002). Steps for implementation of demand forecasting systems-Techniques and case study. Magazine Produto & Producao, 11, 43-64.

Rahman, H., Salma, U., Hossain, M., Khan, T.F. (2016) Revenue Forecasting using Holt–Winters Exponential Smoothing. Research & Reviews: Journal of Statistics, 5(3), 19–25.

Research and Markets. Retrieved from https://www.businesswire.com/news/home/20150112006119/en/Research-and-Markets-Philippines-Abaca-Fiber-Market-Forecast-and-Opportunities-2019.

Ribeiro, R.C.M., Marques, G.T., Junior, P.C. (2019). Holt-Winters Forecasting for Brazilian Natural Gas Production. International Educative Research Foundation and Publisher, 7(6), 119-129. https://doi.org/10.31686/ijier.Vol 7 (6).

Ruekkasaem, L., Sasananan, M. (2018). Forecasting agricultural products prices using time series methods for crop planning. International Journal of Mechanical Engineering and Technology (IJMET), 9(7), 957-971.

Satrio, C.B.A., Darmawan, W., Nadia, B.U., Hanafiah, N. (2021).Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Comput. Sci., 179, 524-532.

Song, X., Xiao, J., Deng, J., Kang, Q., Qhang, Y., and Xu, J. (2016). Time series analysis of influenza incidence in Chinese provinces from 2022 to 2011.

SolarWinds Worldwide (2021). Retrieved from https://orangematter.solarwinds.com/2019/12/15/holt-winters-forecasting-simplified/.

Statista Research Department (2020). Retrieved from https://www.statista.com/statistics/751781/philippines-abaca-production.

Waller, V., Wilsby, A. (2019). Abaca in the Philippines, an overview of a potential important resource for the country: Relating the tensile strength of the single fiber to the microfibrilar angle. Retrieved from http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1352495&dswid=8192.

Winters, P.R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342.



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