Time Series and Forecasting
| Code | School | Level | Credits | Semesters |
| MATH4022 | Mathematical Sciences | 4 | 20 | Spring UK |
- Code
- MATH4022
- School
- Mathematical Sciences
- Level
- 4
- Credits
- 20
- Semesters
- Spring UK
Summary
This course will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real time series data will be described. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. Students will gain experience of using a statistical package and interpreting its output. The course will cover:
- concepts of stationary and non-stationary time-series;
- philosophy of model building in the context of time series analysis;
- simple time series models and their properties;
- the model identification process;
- estimation of parameters;
- assessing the goodness of fit;
- methods for forecasting;
- use of a statistical package.
Target Students
Single Honours MMath students, and students taking MSc in Statistics,MSc in Statistics and Applied Probability, MSc Statistics with Machine Learning, MSc Data Science and MSc Financial and Computational Mathematics in the School of Mathematical Sciences. Natural Sciences Students.
Co-requisites
Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:
Classes
- One 1-hour workshop each week for 11 weeks
- One 2-hour lecture each week for 11 weeks
- One 1-hour lecture each week for 11 weeks
Assessment
- 20% Project 1: Individual investigation using a computer package
- 80% Exam 1 (3-hour): Written examination.
Assessed by end of spring semester
Educational Aims
The purpose of this course is to deepen and broaden the students’ knowledge and experience of statistics by studying the theory and methods used in time series and forecasting.This course is in the Statistics Pathway and builds upon the statistical ideas and methods and probability techniques introduced in thecourseMATH2011 or in thecourse MATH4019. Students will acquire knowledge and skills of relevance to a professional and/or research statistician.Learning Outcomes
A student who completes this course successfully will be able:
- L1 - to state and prove standard results in the time domain relating to the theory, models and methods of time series and forecasting, and apply them to examples;
- L2 - to derive, calculate and explain properties of time domain models and methods;
- L3 - to derive appropriate point and interval estimators, and construct suitable test procedures;
- L4 - to use statistical software packages to fit models to data sets, assess their fit, make predictions, and identify models underlying data sets;
- L5 - to analyse and explain statistical results in the context of time series and forecasting;
- L6 - to present a systematic account of concepts for time series and forecasting;
- L7 - research and synthesize a topic related to forecasting and time series.