MA0367: Time Series Analysis and Forecasting
| School | Cardiff School of Mathematics |
| Department Code | MATHS |
| Module Code | MA0367 |
| External Subject Code | G100 |
| Number of Credits | 10 |
| Level | L6 |
| Language of Delivery | English |
| Module Leader | Dr Andrey Pepelyshev |
| Semester | Spring Semester |
| Academic Year | 2014/5 |
Outline Description of Module
This is a lecture based module designed to acquaint students with the principles of fitting time series models to data and with use of such model in forecasting. The goals of this module are to develop an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing data. This module is aimed at the students who wish to gain a working knowledge of time series and forecasting methods as applied in economics, engineering and the natural and social sciences.
Prerequisite Modules: MA2500 Foundations of Probability and Statistics
Recommended Modules: MA3502 Regression Analysis and Experimental Design
On completion of the module a student should be able to
- fit models for a large variety of data from economics and social sciences.
- appreciate and use modern methods of statistical inference.
- forecasting using ARMA and ARIMA models.
How the module will be delivered
27 – 50 minute lectures
Some handouts will be provided in hard copy or via Learning Central, but students will be expected to take notes of lectures.
Students are also expected to undertake at least 50 hours private study including preparation of solutions to given exercises.
Skills that will be practised and developed
Skills:
Mathematical formulation and modelling.
Forecasting using ARMA, ARIMA models of time series analysis.
Transferable Skills:
Mathematical modelling of various types of problem in time series analysis.
An appreciation of the use of ARMA, ARIMA models for forecasting.
How the module will be assessed
Formative assessment is carried out by means of regular tutorial exercises. Feedback to students on their solutions and their progress towards learning outcomes is provided during tutorial classes.
The in-course element of summative assessment consists of assessed exercises (some to be attempted at home, some in examination conditions) similar in form to the tutorial exercises. This allows students to demonstrate a level of knowledge and skills appropriate to that stage in the module.
The major component of summative assessment is the written examination at the end of the module. This gives students the opportunity to demonstrate their overall achievement of learning outcomes. It also allows them to give evidence of the higher levels of knowledge and understanding required for above average marks.
The examination paper has a choice of three from four equally weighted questions.
Assessment Breakdown
| Type | % | Title | Duration(hrs) |
|---|---|---|---|
| Exam - Spring Semester | 85 | Time Series Analysis & Forecasting | 2 |
| Written Assessment | 5 | Coursework | N/A |
| Class Test | 10 | Time Series Analysis & Forecasting | N/A |
Syllabus content
- Stationary time series.
- Autocovariance, autocorrelation, spectral density.
- Prediction equation in time domain.
- Prediction bounds for normal processes.
- Autoregressive, moving average and ARMA models.
- Sample theory.
- Yule-Walker equations.
- Model identification and estimation in time domain.
- Short memory and long memory series.
- Non-stationary series-ARIMA-models.
Essential Reading and Resource List
Time Series Analysis, Wei, William W.S., Addison-Wesley Publishing Company, 1989.
Background Reading and Resource List
Further material on the time series analysis can be found in many textbooks on stochastic processes.