As for the relevance of this project, it holds a great deal of significance for the oil and gas industry. In fact, time series method does not use information other than the previous value of the data series to be predicted. But most of the time this method is considered naive as we are not using the data that is already available.
The author also emphasizes that the simplicity of the model, straightforward interpretation and limited need for data were most preferred. The advantage of using the Unobserved Component model is that the component of the same type can interact between the different time series. This is the result example that the author compares to the actual graph to see the accuracy of the model compared to the actual data.
The data show that the time series method is one of the reliable methods to forecast demand. This is because the occurrence and frequency are uncertainties. )> China India's future demand for energy is one factor to consider. seriously. )>. On the other hand, microeconomics is the study of the behavior of small economic units, such as that of individual consumers or households.
The moving average (MA) model describes a time series that is a linear function of the current and previous random shock. The distinction in this model is that these random shocks propagate to future values of the time series. Most time series are non-stationary, but some of them can transform into a stationary series through integration.
CHAPTER3 METHODOLOGY
RESEARCH METHODOLOGY
- Problem Statement
- Project Objectives
- Background Study
- Literature Review & Theory
- Data Analysis & Calculation
PROJECT ACTIVITIES
KEY MILESTONE
Week 9: Make the equation and prepare the graph of the model. Week 10: comparison of the result with actual data. Week 11: Pre-EDX, submission of the draft final report and technical document. Week 12: EDX and final report submission. In this chapter, the author presents the findings and data results of the project.
These are the data that have been collected; shows oil usage in Malaysia. However the available data was only from 1980 to 2010 and this data is quite limited that can be used to create the model. The trend of increasing demand for crude oil has already been expected since; consumption is likely to increase further with population growth and rapidly.
But as we can see from the trend, the demand for crude oil in Malaysia is quite stable and not volatile. Dubbed the father of modernization, Mahathir bin Mohamad, who has ushered Malaysia into the era of globalization, fits the theory that rapid industrialization will increase demand for crude oil energy. Other method such as single equation - regression method and multi equation model, can be used and constructed to explain and predict the future with a or.
On the other hand, the time series method did not predict the future movement by relating it to another set of variables. A time series model calculates the pattern in the past movement of a variable and uses that information to predict its future movement.
Inflation Rate in Malaysia
Data that the author collects was not sufficient to do a regression technique; all these data were only from 2003 to 20 I 0. They did not meet the time frame of crude oil demand in Malaysia data, so it is impossible to study the impact of this variable on demand. Inflation occurs when there was an increase in the price of goods, in other words a decrease in the purchasing power of the currency.
Where this method is used only in the demand history to create the modal and predict the future. Based on the generated graph it shows that the demand in Malaysia has a steady growth, but since the forecast graph is moving below the actual demand, there is a possibility that the demand will decrease. The simple moving average model is useful if we believe that a possible value for the series is a simple average of its value over the past 4 years.
Therefore, in the 2nd experiment, the author used an exponentially weighted moving average model. In this case, since the crude oil demand data in Malaysia is quite stable, the author put more weight on a. The forecast also shows that the demand has the possibility to move down in the next 5 years.
Based on both results, we can see that exponential weighted moving average model is much more suitable to represent crude oil demand in Malaysia. Since supply and demand are something that cannot be separated, it is wise to see if production in Malaysia can handle the demand. There is a possibility that production in Malaysia may not be able to meet demand in the next few years.
This is one of the options so that we will be prepared for the worse condition that may come and therefore we will be able to come up with a backup plan. For this reason, Time Series methods are cheap in terms of time and effort. As in the author's opinion, Time series technique specializes for moving average model, it is more accurate for a short period of forecast.
Although it was more complicated and time consuming, having a view of the future can change everything. Paolo Biondi, Danilo Monarca, Arduino Panaro "Simple forecasting model for the demand for farm tractors in Italy, France and the United States".
