Postgraduate Course: Time Series Analysis and Forecasting (MATH11073)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	5	ECTS Credits	2.5
Summary	Filtering, autoregressive and moving average models, ARMA and ARIMA models, forcasting, seasonal variation. The statistical software package SPSS will be used for practical instruction
Course description	week 6 - Introduction, white noise week 7 - Moving average and autoregressive models week 8 - ARMA and ARIMA models week 9 - Forecasting Week 10 - Seasonal variation and other topics

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information

Academic year 2015/16, Not available to visiting students (SS1)		Quota: None
Course Start	Block 4 (Sem 2)
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 50 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 6, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 1, Directed Learning and Independent Learning Hours 31 )
Assessment (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment)	See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)	Time Series Analysis and Forecasting (MATH11073)		2:00

Learning Outcomes
The ability to demonstrate an understanding of the principles behind modern forecasting techniques. The ability to select and appropriate model, to fit parameter values, and to carry out the forecasting calculation, with use of the statistical software package SPSS as appropriate.