At the end of this course, the students; 1) As a tool for decision making under uncertainty is the ability to use appropriate estimation technique to forecast future values 2) Creat new knowledge by integrating information from different disciplines 3) Issues that require expertise in the field of scientific research methods to analyze a problem and, as an independent to envision, develop a solution method, resolve, ability to apply the results to evaluate and, if necessary.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
The role and importance of parameters in decision making process. Dimensions and phases of the forecasting technique. Qualitative and quantitative forecasting methods. Time series and its elements. Indices. Forecasting based on time series.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction, Definition and Importance of Forecasting, Types of Forecasting Techniques
2nd Week
Definition of Time Series and Movements in Time Series
3rd Week
Analysis of Time Series and its Purpose. Analysis of Movements.
4th Week
Stochastic Processes and Box-Jenkins Stochastic Processes.
5th Week
Definitions and Calculations of Autocovariance, Autocorrelation and Partial Autocorrelation Concepts in Times Series
6th Week
Model Selection for Time Series and Parameter Estimation and its Applications
7th Week
Model Selection for Time Series and Parameter Estimation and its Applications
8th Week
Midterm
9th Week
Efficient Parameter Estimation
10th Week
Forecasting in Stationary and Non-stationary Models
11th Week
Interval Estimation in Box-Jenkins Models
12th Week
General Applications
13th Week
General Applications
14th Week
Discussions on Applications
RECOMENDED OR REQUIRED READING
Box, G.E.P, et al, 1994, Time Series Analysis: Forecasting and Control, Holden Day, S Bowerman, B.L., O'Connell, R.T., 1979, Time Series and Forecasting, Duxbury. Vandale, W., 1983, Applied Time Series and Box-Jenksin Models, Academic Pres Inc.
