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Time series analysis on sales data of petroleum in Malaysia

In Malaysia, the energy sector is an important driver of economic growth. We all know that oil and natural gas are the two major industries of the energy market. Malaysia is the second-largest oil producer in Southeast Asia and the world’s third largest exporter of liquefied natural gas (LNG). Petro...

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Main Authors: Sam, Nur Syiffa, Kamarudin, Adina Najwa
Format: Article
Language:English
Published: Universiti Teknologi Malaysia 2023
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/108597/1/AdinaNajwa2023_TimeSeriesAnalysisonSalesData.pdf
http://eprints.utm.my/108597/
https://science.utm.my/procscimath/2023-2/vol-18/
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spelling my.utm.1085972024-11-19T06:30:55Z http://eprints.utm.my/108597/ Time series analysis on sales data of petroleum in Malaysia Sam, Nur Syiffa Kamarudin, Adina Najwa Q Science (General) In Malaysia, the energy sector is an important driver of economic growth. We all know that oil and natural gas are the two major industries of the energy market. Malaysia is the second-largest oil producer in Southeast Asia and the world’s third largest exporter of liquefied natural gas (LNG). Petronas has held exclusive ownership rights over all exploration and production activities related to oil and natural gas in the country since its inception. Throughout the years, there are several issues that affect the sales data of petroleum in Malaysia since the prices are influenced by global supply and demand. This project is based on the time series analysis of monthly sales of petroleum in Petronas Malaysia from the year of 2017 to 2022. The significance of this project is to study the trend and determine the best forecasting model between Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA) for monthly sales of petroleum in Petronas. The performance of the models are then compared by the values of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The outcome showed that, in comparison to Exponential Smoothing, the chosen ARIMA model forecasts more accurately. Universiti Teknologi Malaysia 2023-10 Article PeerReviewed application/pdf en http://eprints.utm.my/108597/1/AdinaNajwa2023_TimeSeriesAnalysisonSalesData.pdf Sam, Nur Syiffa and Kamarudin, Adina Najwa (2023) Time series analysis on sales data of petroleum in Malaysia. Proceedings of Science and Mathematics, 18 (NA). pp. 39-47. ISSN 2756-8857 https://science.utm.my/procscimath/2023-2/vol-18/ NA
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/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Sam, Nur Syiffa
Kamarudin, Adina Najwa
Time series analysis on sales data of petroleum in Malaysia
description In Malaysia, the energy sector is an important driver of economic growth. We all know that oil and natural gas are the two major industries of the energy market. Malaysia is the second-largest oil producer in Southeast Asia and the world’s third largest exporter of liquefied natural gas (LNG). Petronas has held exclusive ownership rights over all exploration and production activities related to oil and natural gas in the country since its inception. Throughout the years, there are several issues that affect the sales data of petroleum in Malaysia since the prices are influenced by global supply and demand. This project is based on the time series analysis of monthly sales of petroleum in Petronas Malaysia from the year of 2017 to 2022. The significance of this project is to study the trend and determine the best forecasting model between Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA) for monthly sales of petroleum in Petronas. The performance of the models are then compared by the values of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The outcome showed that, in comparison to Exponential Smoothing, the chosen ARIMA model forecasts more accurately.
format Article
author Sam, Nur Syiffa
Kamarudin, Adina Najwa
author_facet Sam, Nur Syiffa
Kamarudin, Adina Najwa
author_sort Sam, Nur Syiffa
title Time series analysis on sales data of petroleum in Malaysia
title_short Time series analysis on sales data of petroleum in Malaysia
title_full Time series analysis on sales data of petroleum in Malaysia
title_fullStr Time series analysis on sales data of petroleum in Malaysia
title_full_unstemmed Time series analysis on sales data of petroleum in Malaysia
title_sort time series analysis on sales data of petroleum in malaysia
publisher Universiti Teknologi Malaysia
publishDate 2023
url http://eprints.utm.my/108597/1/AdinaNajwa2023_TimeSeriesAnalysisonSalesData.pdf
http://eprints.utm.my/108597/
https://science.utm.my/procscimath/2023-2/vol-18/
_version_ 1816130077342040064
score 13.244413

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