Sheffield Hallam University
School of Computing and Management Science
Msc. Project specification
Student: Hassan S. Ammora
Day Tel No.: 0121 2426999
Email: Hassan.S.Ammora@ Student.shu.ac.uk
Title of Project:
The proposed project title is :
SHORT -TERM FORECASTING OF
'DERV' FUEL CONSUMPTION IN
TRANSPORTATION
This might be changed to Medium or Long Term Forecasting …. Depending on
discussion with the supervisor.
Objective of Project:
The project aims to design and develop model(s) which describe consumption of
"DERV" fuel in transportation for forecasting use.
The main objective is to obtain useful method of consumption forecast to see whether
the consumption of this type of fuel in the near future will increase or decrease. This
will help the decision makers in making suitable decisions regarding 'DERV' fuel
consumption.
For monthly data the period of forecasting will probably be from Jan-2005 up to Dec2005
For annual data the period will be between 2001 and 2005.
METHODOLOGY:
The method is to investigate and analyze the sample period data using different
time series and forecasting techniques for both monthly and annual data to obtain our
proposed forecasts.
The forecasting techniques are to be chosen from the following list:
1.
2.
3.
4.
5.
6.
7.
Decomposition method.
Simple exponential smoothing.
Double exponential smoothing.
Holt-winter’s exponential smoothing.
Simple linear regression.
Multiple regressions.
Box-jenkin’s ARIMA model.
RELEVANT LITRATURE:
Up to date I couldn't find a literature about the same topic, but there are some of
similar topics. For example:
1. Short-term Forecasting of Domestic Electricity consumption in Hong Kong
by Ha, Hau Yee, Gloria
which have used forecasting techniques such as; Decomposition, Winter's
Smoothing and Box Jenkins methods.
2. Medium Term Forecasting of Air Traffic Movements.
By D T Marsh
Who found that Box-Jenkins methods are relevant for medium term forecasting.
DATA AVAILABLE:
Our data is historical data and is available as publications issued by Office for
National statistics (ONS). The only explanatory variable we have is TIME.
Data Examining:
Data sets are to be examined by plotting to see whether there is any seasonality,
trend, ….etc in the data. Also we will see if the data need to be adjusted.
INVESTIGATION:
Initially we will look at the data as a whole then we will decide which period of
time is suitable to fulfill our objectives and eventually we will find model(s) to
forecast the 12 months. Results to be compared with actual data and residuals are to
be analyzed.
TIME SERIES AND FORECASTING TECHNIQUES:
Techniques and Methods:
A suitable and convenient method(s) will be selected from the following list:
8. Decomposition method.
9. Simple exponential smoothing.
10. Double exponential smoothing.
11. Holt-winter’s exponential smoothing.
12. Simple linear regression.
13. Multiple regressions.
14. Box-jenkin’s ARIMA model.
1. Dcomposition method.
In this method the assumption is made that three systematic components :
seasonal,
trend and cyclical plus random component are forming the time series.
Seasonality:
They can be quarterly, monthly or weekly. They repeat themselves year after
the other and one can predict them. Unemployment and insurance claim
are examples.
Trend :
Trend component is continuous movement over an extended period of time.
It can be linear or quadratic line. For example, changes in technology,
productivity and changes in foreign competition among others.
Cyclical :
This is a long duration movement (more than one year). It results from
changes in general economic activity.
Irregular :
Irregular variations can not be predicted, it can be strike, war, earthquakes,
they are not repeated.
Relationship between times series components:
Relationship between these components can be ADDITIVE ,i.e.
YT = St + Tt + Ct + It
or MULTIPLICATIVE , i.e.
Yt = St . Tt . Ct . It
where :
Yt : time series,
St : seasonal components,
Tt : trend
“
Ct : cyclical
“
and
It : irregular
“
.
2. Simple exponential smoothing :
This method simplifies forecasting calculations and dosen’t need all data
because the information that we need is in the last forecast. Random
fluctuations can be removed using this method.
3. Double exponential smoothing :
The results from simple exponential smoothing method are smoothed once
more.
This method is used when there is a trend in the time series.
4. Holt-winter’s exponential :
When a time series have trend and seasonal variations, this method can
be applied.
5. Simple linear regression :
If the relationship between one independent variable (x) and the dependent
variable (y) takes the form
y = a + bx where a and b are constants,
then this model known as simple linear regression.
6. Multiple regression :
If there are more than on independent variable which affect the dependent
variable, then the multiple regression model can be used.
7. Box-jenkin’s ARIMA model :
ARIMA stands for AutoRegressive Integrated Moving Average.
Integrated refer to differencig of time series.
Autoregressive (AR) is in the form :
Yt = b + b1Yt-1 + b2Yt-2 + ………….+ bmYt-m
The simplest random ARIMA model is in the form :
Yt = u +et
where there is no Autocorrelation,
no autoregressive ,
no differencing and
no moving average.
The time series is completely RANDOM.
Steps in ARIMA model:
1. Identification.
Is the time series stationary or no?
Are the data seasonal?
Identify the model.
2. Estimate the parameters.
3. Check whether the model is adequate.
Test residuals using X-test.
4. Forecast.
ARIMA model is very powerful method for forecasting but unless it is well
understood it can not be applied.
PROJECT PROCEDURES:
1.
2.
3.
4.
5.
6.
7.
8.
9.
Collecting time series observations.
Data plotting and check it for seasonality, trend, … etc..
Data adjustment ( if needed).
Comparison of forecasting methods.
Model selection and fitting.
Obtain forecasts.
Compare between actual and forecasted observations and residual analysis.
Conclusions and recommendations.
Future use.