Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast mi...

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Main Authors:	Veerasak Punyapornwithaya, Katechan Jampachaisri, Kunnanut Klaharn, Chalutwan Sansamur
Format:	article
Language:	EN
Published:	Frontiers Media S.A. 2021
Subjects:	milk production forecast time-series model hybrid model decision Thailand Veterinary medicine SF600-1100
Online Access:	https://doaj.org/article/a330462254e14e89834ee92f7f7f9d6c
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spelling	oai:doaj.org-article:a330462254e14e89834ee92f7f7f9d6c2021-12-01T16:34:52ZForecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models2297-176910.3389/fvets.2021.775114https://doaj.org/article/a330462254e14e89834ee92f7f7f9d6c2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fvets.2021.775114/fullhttps://doaj.org/toc/2297-1769Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.Veerasak PunyapornwithayaVeerasak PunyapornwithayaKatechan JampachaisriKunnanut KlaharnChalutwan SansamurFrontiers Media S.A.articlemilk productionforecasttime-series modelhybrid modeldecisionThailandVeterinary medicineSF600-1100ENFrontiers in Veterinary Science, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	milk production forecast time-series model hybrid model decision Thailand Veterinary medicine SF600-1100
spellingShingle	milk production forecast time-series model hybrid model decision Thailand Veterinary medicine SF600-1100 Veerasak Punyapornwithaya Veerasak Punyapornwithaya Katechan Jampachaisri Kunnanut Klaharn Chalutwan Sansamur Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models
description	Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.
format	article
author	Veerasak Punyapornwithaya Veerasak Punyapornwithaya Katechan Jampachaisri Kunnanut Klaharn Chalutwan Sansamur
author_facet	Veerasak Punyapornwithaya Veerasak Punyapornwithaya Katechan Jampachaisri Kunnanut Klaharn Chalutwan Sansamur
author_sort	Veerasak Punyapornwithaya
title	Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models
title_short	Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models
title_full	Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models
title_fullStr	Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models
title_full_unstemmed	Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models
title_sort	forecasting of milk production in northern thailand using seasonal autoregressive integrated moving average, error trend seasonality, and hybrid models
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/a330462254e14e89834ee92f7f7f9d6c
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Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

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