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Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning

This study focuses on identifying and analyzing spending trend profiles and developing the per capita consumption models to forecast the fresh agro-food per capita consumption in Malaysia. Previous published works have looked at statistical and machine learning methods to forecast the demand of agro...

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Main Authors:	Alfred, Rayner, Leikson, Christylyn, Boniface, Bonaventure, Tanakinja, Geoffrey Harvey, Kamu, Assis, Kogid, Mori, Sondoh, Stephen L., Mohd Nawi, Nolila, Arumugam, Nalini, Andrias, Ryan Macdonell
Format:	Article
Published:	Hindawi 2022
Online Access:	http://psasir.upm.edu.my/id/eprint/102201/ https://www.hindawi.com/journals/misy/2022/6106557/
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spelling	my.upm.eprints.1022012023-07-06T05:22:35Z http://psasir.upm.edu.my/id/eprint/102201/ Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning Alfred, Rayner Leikson, Christylyn Boniface, Bonaventure Tanakinja, Geoffrey Harvey Kamu, Assis Kogid, Mori Sondoh, Stephen L. Mohd Nawi, Nolila Arumugam, Nalini Andrias, Ryan Macdonell This study focuses on identifying and analyzing spending trend profiles and developing the per capita consumption models to forecast the fresh agro-food per capita consumption in Malaysia. Previous published works have looked at statistical and machine learning methods to forecast the demand of agro-food such as ARIMA and SVM methods. However, ordinary least squares (OLS) and neural network (NN) methods have shown better results in modelling time series data. For that reason, the primary objective of this study is to model and forecast the consumption per capita (PCC) of several selected fresh agro-food commodities in Malaysia using the OLS and NN methods. The secondary objectives of the paper include investigating the performance of OLS against NNs with three different topologies, discussing the correlation between Malaysia GDP per capita and the agro-food commodity PCC, and finally assessing whether the PCC data are increasing over time or decreasing over time and whether the trend in either direction is statistically significant by using the Mann–Kendall statistical test. Based on the results of the agro-food consumption per capita (PCC) forecasting, several critical agro-food commodities are also identified in this work. The material of the study consists of the per capita consumption of thirty-three (33) agro-food items that can be categorized into rice, livestock, vegetables, fisheries, and fruits, total gross domestic product (GDP) per capita, and the total population of Malaysia between 2010 and 2017. Based on the results obtained, the neural network () model was found to produce the lowest total MSE of 17.95, for all 33 fresh agro-food investigated in this study. Several agro-food commodities have been identified as having significant positive (e.g., rice, spinach, cabbage, celery cabbage, eggplant, cucumber, poultry, lamb, squid, tuna, star fruit, jackfruit, durian, sweet corn, and coconut) or negative (e.g., pork, mackerel, papaya, guava, mangosteen, pineapple, banana, rambutan, and watermelon) trends using the Mann–Kendall trend test. This study also demonstrated that the production of critical agro-food commodities (e.g., rice, chili, cabbage, celery cabbage, poultry (chicken/duck), beef, lamb, crab, mango, and coconut) should be improved to ensure self-sufficiency ratios (SSRs) of more than 100% to accommodate the increased projected consumption in Malaysia by the year 2025. This paper concludes that neural network methods produce better prediction, and future works include forecasting agro-food demand based on other independent variables such as weather conditions, disease outbreak, and stock market trends. There is a need to explore further the capability of ensemble models or hybrid models based on deep learning methods using multi-source data, as these have been shown to improve the performance of the base model. With these ensemble models combined with multi-source data, a more comprehensive analysis of the PCC can be obtained. Hindawi 2022-11-12 Article PeerReviewed Alfred, Rayner and Leikson, Christylyn and Boniface, Bonaventure and Tanakinja, Geoffrey Harvey and Kamu, Assis and Kogid, Mori and Sondoh, Stephen L. and Mohd Nawi, Nolila and Arumugam, Nalini and Andrias, Ryan Macdonell (2022) Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning. Mobile Information Systems, 2022. art. no. 6106557. pp. 1-17. ISSN 1574-017X https://www.hindawi.com/journals/misy/2022/6106557/ 10.1155/2022/6106557
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
description	This study focuses on identifying and analyzing spending trend profiles and developing the per capita consumption models to forecast the fresh agro-food per capita consumption in Malaysia. Previous published works have looked at statistical and machine learning methods to forecast the demand of agro-food such as ARIMA and SVM methods. However, ordinary least squares (OLS) and neural network (NN) methods have shown better results in modelling time series data. For that reason, the primary objective of this study is to model and forecast the consumption per capita (PCC) of several selected fresh agro-food commodities in Malaysia using the OLS and NN methods. The secondary objectives of the paper include investigating the performance of OLS against NNs with three different topologies, discussing the correlation between Malaysia GDP per capita and the agro-food commodity PCC, and finally assessing whether the PCC data are increasing over time or decreasing over time and whether the trend in either direction is statistically significant by using the Mann–Kendall statistical test. Based on the results of the agro-food consumption per capita (PCC) forecasting, several critical agro-food commodities are also identified in this work. The material of the study consists of the per capita consumption of thirty-three (33) agro-food items that can be categorized into rice, livestock, vegetables, fisheries, and fruits, total gross domestic product (GDP) per capita, and the total population of Malaysia between 2010 and 2017. Based on the results obtained, the neural network () model was found to produce the lowest total MSE of 17.95, for all 33 fresh agro-food investigated in this study. Several agro-food commodities have been identified as having significant positive (e.g., rice, spinach, cabbage, celery cabbage, eggplant, cucumber, poultry, lamb, squid, tuna, star fruit, jackfruit, durian, sweet corn, and coconut) or negative (e.g., pork, mackerel, papaya, guava, mangosteen, pineapple, banana, rambutan, and watermelon) trends using the Mann–Kendall trend test. This study also demonstrated that the production of critical agro-food commodities (e.g., rice, chili, cabbage, celery cabbage, poultry (chicken/duck), beef, lamb, crab, mango, and coconut) should be improved to ensure self-sufficiency ratios (SSRs) of more than 100% to accommodate the increased projected consumption in Malaysia by the year 2025. This paper concludes that neural network methods produce better prediction, and future works include forecasting agro-food demand based on other independent variables such as weather conditions, disease outbreak, and stock market trends. There is a need to explore further the capability of ensemble models or hybrid models based on deep learning methods using multi-source data, as these have been shown to improve the performance of the base model. With these ensemble models combined with multi-source data, a more comprehensive analysis of the PCC can be obtained.
format	Article
author	Alfred, Rayner Leikson, Christylyn Boniface, Bonaventure Tanakinja, Geoffrey Harvey Kamu, Assis Kogid, Mori Sondoh, Stephen L. Mohd Nawi, Nolila Arumugam, Nalini Andrias, Ryan Macdonell
spellingShingle	Alfred, Rayner Leikson, Christylyn Boniface, Bonaventure Tanakinja, Geoffrey Harvey Kamu, Assis Kogid, Mori Sondoh, Stephen L. Mohd Nawi, Nolila Arumugam, Nalini Andrias, Ryan Macdonell Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning
author_facet	Alfred, Rayner Leikson, Christylyn Boniface, Bonaventure Tanakinja, Geoffrey Harvey Kamu, Assis Kogid, Mori Sondoh, Stephen L. Mohd Nawi, Nolila Arumugam, Nalini Andrias, Ryan Macdonell
author_sort	Alfred, Rayner
title	Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning
title_short	Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning
title_full	Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning
title_fullStr	Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning
title_full_unstemmed	Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning
title_sort	modeling and forecasting fresh agro-food commodity consumption per capita in malaysia using machine learning
publisher	Hindawi
publishDate	2022
url	http://psasir.upm.edu.my/id/eprint/102201/ https://www.hindawi.com/journals/misy/2022/6106557/
_version_	1772813430241099776
score	13.244413

Modeling and forecasting fresh agro-food commodity consumption per capita in Malaysia using machine learning

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