STAT40700 Time Series Analysis - Act App

Academic Year 2021/2022

This course provides an opportunity for students to learn some basic techniques in Time Series Analysis. A Time Series is a set of measurements taken at regular intervals over a period of time. Time series pervade the worlds of economics and finance e.g. equity prices, (CPI) inflation, GNP, GDP, derivative prices, oil prices, etc. Although the techniques of Time Series Analysis are used by many people, from meteorologists to astronomers, this course will mainly focus on examples in the world of economics and finance. This course will show how to model such data and how using those models forecasts and predictions can be made.Time series analysis is not a new subject, however there have been many advances in recent years and the 2003 Nobel prize in Economics was awarded to Engle and Granger for their work in time-series econometrics. This course will cover both traditional methods and more modern approaches to Time Series Analysis. Upon completion of this course students should have mastered techniques that are extremely valuable for careers in the analysis of economic and financial data. Topics covered include, among others: Stationarity, ARIMA models, Parameter Estimation, Forecasting and Cointegration. Additional topics may be included which may vary from year to year.

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Curricular information is subject to change

Learning Outcomes:

Upon completion of this module students will be able to:
1. Identify the stationarity properties of a time series,
2. Model the time series using Box-Jenkins ARIMA techniques,
3. Estimate parameters for ARIMA models using a variety of procedures,
4. Produce forecasts for a given time series,
5. Be familiar with additional topics such as cointegration, vector auto regressive models...

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

10

Autonomous Student Learning

75

Lectures

18

Tutorial

12

Computer Aided Lab

4

Total

119

Approaches to Teaching and Learning:
Lectures pre-recorded and posted to the VLE, with face-to-face sessions twice weekly to discuss the recorded material. Tutorials in smaller groups to work on practical problems, computer labs with demonstrator to work through coding examples. Solutions to tutorial and lab sheets posted after each class as well as worked solutions to assignments. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Familiarity with basic probability concepts such as Probability distribution, Expectation, Variance, Covariance and Correlation. Knowledge of the main probability distributions (normal distribution, chi-square, ...). Basic linear algebra (vectors, matrices).

Learning Recommendations:

Students should have a knowledge of statistical inference at a level equivalent to that which would be achieved upon completion of "Inferential Statistics" STAT20100.

Basic knowledge in linear algebra (vectors, matrices). Knowledge of linear models and least square estimation which would be achieved upon completion of "Data Modelling for Science" STAT20070 or "Predictive Analytics I" STAT30240 would be beneficial.


Module Requisites and Incompatibles
Incompatibles:
STAT30010 - Time Series Analysis, STAT40860 - Time Series (online)

Additional Information:
Students must have confidence and experience in the theory and use of expected values and covariances of random variables.


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets. Throughout the Trimester n/a Graded No

30

Examination: 2 hour end of semester exam 2 hour End of Trimester Exam No Graded No

70


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

J.D. Cryer & K-S. Chan, Time Series Analysis: With Applications in R, Springer, 2008 (2nd edition).
Name Role
Mr James Hannon Tutor
Lukasz Kaczmarczyk Tutor
Hardeep Kaur Tutor
Catherine Mahoney Tutor
Brian O'Sullivan Tutor
Silvia Scarpa Tutor
Niyati Seth Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Autumn
     
Lecture Offering 1 Week(s) - Autumn: All Weeks Mon 11:00 - 11:50
Tutorial Offering 1 Week(s) - Autumn: Even Weeks Thurs 17:00 - 17:50
Tutorial Offering 1 Week(s) - Autumn: Even Weeks Tues 17:00 - 17:50
Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 12:00 - 12:50
Laboratory Offering 1 Week(s) - 5, 7, 9, 11 Tues 17:00 - 17:50
Laboratory Offering 2 Week(s) - 5, 7, 9, 11 Wed 17:00 - 17:50
Laboratory Offering 3 Week(s) - 5, 7, 9, 11 Wed 15:00 - 15:50
Laboratory Offering 4 Week(s) - 5, 7, 9, 11 Tues 17:00 - 17:50
Laboratory Offering 5 Week(s) - 5, 7, 9, 11 Wed 17:00 - 17:50
Laboratory Offering 6 Week(s) - 5, 9, 11 Wed 15:00 - 15:50
Laboratory Offering 6 Week(s) - 7 Wed 15:00 - 15:50
Autumn