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Forecasting modelling of cockles in Malaysia by using time series analysis

Cockle farmed in Malaysia are from Anadara genes and Arcidae family which known as blood cockle. Normally, it was found in the farmed around mangrove estuary areas in the muddy and sandy shores. This study aims to predict the production of cockle to ensure sure the cockle supplies are synchronised w...

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Main Authors: Ngali, Zamani, Jemain, Noratika Budi, Chang, An Wee, Abdol Rahman, Mohd Nasrull, Kaharuddin, Muhammad Zulhilmi, Khairu Razak, Siti Badriah
Format: Article
Published: Science Publishing Corporation 2018
Subjects:
TA Engineering (General). Civil engineering (General)
QA299.6-433 Analysis
TA329-348 Engineering mathematics. Engineering analysis
Online Access:http://eprints.uthm.edu.my/5124/
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spelling my.uthm.eprints.51242022-01-06T01:48:23Z http://eprints.uthm.edu.my/5124/ Forecasting modelling of cockles in Malaysia by using time series analysis Ngali, Zamani Jemain, Noratika Budi Chang, An Wee Abdol Rahman, Mohd Nasrull Kaharuddin, Muhammad Zulhilmi Khairu Razak, Siti Badriah TA Engineering (General). Civil engineering (General) QA299.6-433 Analysis TA329-348 Engineering mathematics. Engineering analysis Cockle farmed in Malaysia are from Anadara genes and Arcidae family which known as blood cockle. Normally, it was found in the farmed around mangrove estuary areas in the muddy and sandy shores. This study aims to predict the production of cockle to ensure sure the cockle supplies are synchronised with the demand. Then, based on the demand, the prediction result could be used to make decision either to import or export the cockle. The data were taken from the Department of Fisheries Malaysia (DFM) and it has cyclic pattern data. There are two methods used in this study which are Holt-Linear method and Auto regressive moving average (ARMA). In determining the best fitted model between the two methods, the mean square error (MSE) values will be compared and the lowest value of MSE will assign as the best model. Result shows that ARMA(1,1) is the best model compared to Holt-Linear. Therefore, ARMA(1,1) model will be used to forecast the production of cockle in Malaysia. Science Publishing Corporation 2018 Article PeerReviewed Ngali, Zamani and Jemain, Noratika Budi and Chang, An Wee and Abdol Rahman, Mohd Nasrull and Kaharuddin, Muhammad Zulhilmi and Khairu Razak, Siti Badriah (2018) Forecasting modelling of cockles in Malaysia by using time series analysis. International Journal of Engineering & Technology, 7 (4.3). pp. 488-491. ISSN 2227-524X
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
topic TA Engineering (General). Civil engineering (General)
QA299.6-433 Analysis
TA329-348 Engineering mathematics. Engineering analysis
spellingShingle TA Engineering (General). Civil engineering (General)
QA299.6-433 Analysis
TA329-348 Engineering mathematics. Engineering analysis
Ngali, Zamani
Jemain, Noratika Budi
Chang, An Wee
Abdol Rahman, Mohd Nasrull
Kaharuddin, Muhammad Zulhilmi
Khairu Razak, Siti Badriah
Forecasting modelling of cockles in Malaysia by using time series analysis
description Cockle farmed in Malaysia are from Anadara genes and Arcidae family which known as blood cockle. Normally, it was found in the farmed around mangrove estuary areas in the muddy and sandy shores. This study aims to predict the production of cockle to ensure sure the cockle supplies are synchronised with the demand. Then, based on the demand, the prediction result could be used to make decision either to import or export the cockle. The data were taken from the Department of Fisheries Malaysia (DFM) and it has cyclic pattern data. There are two methods used in this study which are Holt-Linear method and Auto regressive moving average (ARMA). In determining the best fitted model between the two methods, the mean square error (MSE) values will be compared and the lowest value of MSE will assign as the best model. Result shows that ARMA(1,1) is the best model compared to Holt-Linear. Therefore, ARMA(1,1) model will be used to forecast the production of cockle in Malaysia.
format Article
author Ngali, Zamani
Jemain, Noratika Budi
Chang, An Wee
Abdol Rahman, Mohd Nasrull
Kaharuddin, Muhammad Zulhilmi
Khairu Razak, Siti Badriah
author_facet Ngali, Zamani
Jemain, Noratika Budi
Chang, An Wee
Abdol Rahman, Mohd Nasrull
Kaharuddin, Muhammad Zulhilmi
Khairu Razak, Siti Badriah
author_sort Ngali, Zamani
title Forecasting modelling of cockles in Malaysia by using time series analysis
title_short Forecasting modelling of cockles in Malaysia by using time series analysis
title_full Forecasting modelling of cockles in Malaysia by using time series analysis
title_fullStr Forecasting modelling of cockles in Malaysia by using time series analysis
title_full_unstemmed Forecasting modelling of cockles in Malaysia by using time series analysis
title_sort forecasting modelling of cockles in malaysia by using time series analysis
publisher Science Publishing Corporation
publishDate 2018
url http://eprints.uthm.edu.my/5124/
_version_ 1738581340347957248
score 13.211869

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