A comparative study of forecasting container throughput through time series analysis

A comparative study of forecasting container throughput through time series analysis

This paper shows different techniques used in the operational research to encounter with forecasting the total container throughput handling. Each techniques approached has its objective and constraints regarding to the research problem. The container throughput is responsible for large investments...

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Main Authors: Raman, Siti Nurfaizzah, Sufahani, Suliadi, Ismail, Zuhaimy
Format: Article
Language:English
Published: Vibus Innovative Publishing Solution 2017
Subjects:
QA273-280 Probabilities. Mathematical statistics
Online Access:http://eprints.uthm.edu.my/4118/1/AJ%202017%20%28570%29.pdf
http://eprints.uthm.edu.my/4118/
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Institution: Universiti Tun Hussein Onn Malaysia
Language: English
id my.uthm.eprints.4118
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spelling my.uthm.eprints.41182021-11-25T04:06:53Z http://eprints.uthm.edu.my/4118/ A comparative study of forecasting container throughput through time series analysis Raman, Siti Nurfaizzah Sufahani, Suliadi Ismail, Zuhaimy QA273-280 Probabilities. Mathematical statistics This paper shows different techniques used in the operational research to encounter with forecasting the total container throughput handling. Each techniques approached has its objective and constraints regarding to the research problem. The container throughput is responsible for large investments in port infrastructure development as the aims is to established a sufficiently accurate forecasting decision support system since they try to follow the global trends in the optimization of port operations and facilities. Vibus Innovative Publishing Solution 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/4118/1/AJ%202017%20%28570%29.pdf Raman, Siti Nurfaizzah and Sufahani, Suliadi and Ismail, Zuhaimy (2017) A comparative study of forecasting container throughput through time series analysis. International Journal of Information Research and Review, 4 (10). pp. 1-4. ISSN 2349-9141
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/
language English
topic QA273-280 Probabilities. Mathematical statistics
spellingShingle QA273-280 Probabilities. Mathematical statistics
Raman, Siti Nurfaizzah
Sufahani, Suliadi
Ismail, Zuhaimy
A comparative study of forecasting container throughput through time series analysis
description This paper shows different techniques used in the operational research to encounter with forecasting the total container throughput handling. Each techniques approached has its objective and constraints regarding to the research problem. The container throughput is responsible for large investments in port infrastructure development as the aims is to established a sufficiently accurate forecasting decision support system since they try to follow the global trends in the optimization of port operations and facilities.
format Article
author Raman, Siti Nurfaizzah
Sufahani, Suliadi
Ismail, Zuhaimy
author_facet Raman, Siti Nurfaizzah
Sufahani, Suliadi
Ismail, Zuhaimy
author_sort Raman, Siti Nurfaizzah
title A comparative study of forecasting container throughput through time series analysis
title_short A comparative study of forecasting container throughput through time series analysis
title_full A comparative study of forecasting container throughput through time series analysis
title_fullStr A comparative study of forecasting container throughput through time series analysis
title_full_unstemmed A comparative study of forecasting container throughput through time series analysis
title_sort comparative study of forecasting container throughput through time series analysis
publisher Vibus Innovative Publishing Solution
publishDate 2017
url http://eprints.uthm.edu.my/4118/1/AJ%202017%20%28570%29.pdf
http://eprints.uthm.edu.my/4118/
_version_ 1738581209849528320

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