Forecasting model of fuel price in Malaysia / Muhammad Asyraf Roslan , Muhammad Shazwan Tun Sri Lanang and Wan Mohamad Omar Syafiq Wan Mohamad Nasir

Recorded information and reports have demonstrated that fuel price changes can give an impact on the worldwide monetary movement and execution. Sharp increments in fuel costs is related to activating high expansion and being a danger to monetary strength. Undoubtedly, numerous examinations have disc...

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Bibliographic Details
Main Authors: Roslan, Muhammad Asyraf, Tun Sri Lanan, Muhammad Shazwan, Wan Mohamad Nasir, Wan Mohamad Omar Syafiq
Format: Student Project
Language:English
Published: 2019
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/50010/1/50010.pdf
https://ir.uitm.edu.my/id/eprint/50010/
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Summary:Recorded information and reports have demonstrated that fuel price changes can give an impact on the worldwide monetary movement and execution. Sharp increments in fuel costs is related to activating high expansion and being a danger to monetary strength. Undoubtedly, numerous examinations have discovered that most monetary subsidence can be clarified by extraordinary increments in fuel costs. Because of the noteworthy impact of fuel costs on the worldwide economy, subjects identified with fuel value changes stay critical and keep on drawing in studies. In paper, the fuel issue is explored when it is the basic source of vitality used in the generation of many assembly items, then changes in fuel costs can affect the entire economy in the country. Next, the problem programming model suitable for use in its internal function has been discussed. To solve this problem, the satisfaction function for the interval problem is used on objective function and using Granger test to obtain stationary observation solution. Next, the Box-Jenkins is used to get the model. The sensitivity analysis of the proposed decision model is implemented. The ARIMA ( 1,2, I) has been chosen as the best model chosen through Box-Jenkins characteristic. Therefore it can be concluded that, ARfMA ( 1,2, I) can be used in terms of model characteristics and predictions.