At the end of this course, the students; 1) Specific data considerations, time series and its patterns 2) Basic and advanced Forecasting Models from time series data
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
There is no recommended optional program component for this course.
COURSE DEFINITION
This course covers the following topics: Forecasting perspective; Basic Forecasting Tools; Forecasting with Regression: Simple and Multiple Linear Regression analyses; Time Series Decomposition; Box-Jenkins Methodology (ARIMA); Advanced Forecasting Models; Forecasting the long term; Judgmental Forecasting and adjustments; the Use of forecasting Methods in practice.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction to Forecasting
2nd Week
Forecasting Process, Data Considerations, Model Selection
3rd Week
Moving Averages and Exponential Smoothing Methods
4th Week
Exponential Smoothing Methods
5th Week
Forecasting with Simple Linear Regression
6th Week
Forecasting with Simple Linear Regression
7th Week
Multiple Regression
8th Week
MIDTERM I
9th Week
Time Series Decomposition Box-Jenkins (ARIMA) Type Forecasting Methods
10th Week
Box-Jenkins (ARIMA) Type Forecasting
11th Week
Methods
12th Week
Forecasting the long term
13th Week
Judgmental Forecasting and adjustments the Use of forecasting Methods in practice
14th Week
MIDTERM II
RECOMENDED OR REQUIRED READING
Forecasting Methods and Applications, Makridakis/Wheelwright/Hyndman, 3rd edition, John Wiley & Sons, Inc., New York, 1998. Business Forecasting, Hanke/Wichern/Reitsch, 7th edition, Prentice Hall, 2001. Business Forecasting, Wilson/Keating, 2nd edition, Irwin, 1994. Time Series Analysis: Forecasting and Control, Box/Jenkins/Reinsell, 3rd edition, Englewoods Cliffs, Prentice-Hall, 2001. Business Forecasting, Hanke/Wichern/Reitsch, 7th edition, Prentice Hall, 2001.
