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A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms

Crude palm oil (CPO) price prediction plays an important role in the agricultural economic development. It requires an in-depth knowledge in both economics and agricultural domain. The aim of this paper is to propose a CPO price prediction model to help the plantation organizations in the palm oil s...

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Main Authors: Kanchymalay, Kasturi, Salim, N., Krishnan, Ramesh, Hashim, U. R., Mas Aina, M. B., Indradevi, Indradevi, Mutasem, Jarrah
Format: Article
Published: World Academy of Research in Science and Engineering 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/90329/
http://dx.doi.org/10.30534/ijatcse/2020/238942020
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spelling my.utm.903292021-04-30T14:31:00Z http://eprints.utm.my/id/eprint/90329/ A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms Kanchymalay, Kasturi Salim, N. Krishnan, Ramesh Hashim, U. R. Mas Aina, M. B. Indradevi, Indradevi Mutasem, Jarrah QA75 Electronic computers. Computer science Crude palm oil (CPO) price prediction plays an important role in the agricultural economic development. It requires an in-depth knowledge in both economics and agricultural domain. The aim of this paper is to propose a CPO price prediction model to help the plantation organizations in the palm oil sector to effectively anticipate CPO price fluctuations and managing the resources more effectively. The CPO price behavior are non-linear in nature, thus prediction is very difficult. In this paper, a recurrent network, Long Short Term Memory (LSTM) based CPO price prediction system is compared with artificial neural network (ANN) and Holt-Winter method. The findings of this study shows that the LSTM based forecasting model outperformed other models in forecasting the CPO price movement. This study recommends that a LSTM based forecasting could better help the farmer and planters in the agriculture sector in managing the demand of CPO and the operation processes for a better return on investment. World Academy of Research in Science and Engineering 2020 Article PeerReviewed Kanchymalay, Kasturi and Salim, N. and Krishnan, Ramesh and Hashim, U. R. and Mas Aina, M. B. and Indradevi, Indradevi and Mutasem, Jarrah (2020) A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4). pp. 5802-5806. ISSN 2278-3091 http://dx.doi.org/10.30534/ijatcse/2020/238942020
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Kanchymalay, Kasturi
Salim, N.
Krishnan, Ramesh
Hashim, U. R.
Mas Aina, M. B.
Indradevi, Indradevi
Mutasem, Jarrah
A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms
description Crude palm oil (CPO) price prediction plays an important role in the agricultural economic development. It requires an in-depth knowledge in both economics and agricultural domain. The aim of this paper is to propose a CPO price prediction model to help the plantation organizations in the palm oil sector to effectively anticipate CPO price fluctuations and managing the resources more effectively. The CPO price behavior are non-linear in nature, thus prediction is very difficult. In this paper, a recurrent network, Long Short Term Memory (LSTM) based CPO price prediction system is compared with artificial neural network (ANN) and Holt-Winter method. The findings of this study shows that the LSTM based forecasting model outperformed other models in forecasting the CPO price movement. This study recommends that a LSTM based forecasting could better help the farmer and planters in the agriculture sector in managing the demand of CPO and the operation processes for a better return on investment.
format Article
author Kanchymalay, Kasturi
Salim, N.
Krishnan, Ramesh
Hashim, U. R.
Mas Aina, M. B.
Indradevi, Indradevi
Mutasem, Jarrah
author_facet Kanchymalay, Kasturi
Salim, N.
Krishnan, Ramesh
Hashim, U. R.
Mas Aina, M. B.
Indradevi, Indradevi
Mutasem, Jarrah
author_sort Kanchymalay, Kasturi
title A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms
title_short A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms
title_full A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms
title_fullStr A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms
title_full_unstemmed A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms
title_sort comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms
publisher World Academy of Research in Science and Engineering
publishDate 2020
url http://eprints.utm.my/id/eprint/90329/
http://dx.doi.org/10.30534/ijatcse/2020/238942020
_version_ 1698696920195661824
score 13.211869

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