Close
Description
This module aims to introduce methods of finding and extrapolating patterns in time-ordered sequences. It is primarily intended for third and fourth year undergraduates and taught postgraduates registered on the degree programmes offered by the Department of Statistical Science (including the MASS programmes).听For these students, the academic prerequisites for this module are met either through earlier compulsory study within (UG) or successful admission to (PGT) their current programme.听It also serves as an optional module for students taking the Mathematics and Statistics stream of the Natural Sciences degree (with prerequisite: STAT0005).
Intended Learning Outcomes
- be familiar听with the most commonly-used models for time series;
- be able to derive properties of time series models;
- be able to select, fit, check and use appropriate models for time-ordered data sequences;
- understand and be able to interpret the output from the time series module of a variety of standard software packages.
Applications - Time series data take the form of observations of one or more processes over time, where the structure of the temporal dependence between observations is the object of interest. Such data arise in many application areas including economics, engineering and the natural and social sciences. The use of historical information to estimate characteristics of observed processes, and to construct forecasts together with assessments of the associated uncertainty, is widespread in these application areas.
Indicative Content - Forecasting as the discovery and extrapolation of patterns in time ordered data. Revision of descriptive measures for multivariate distributions. Descriptive techniques for time series. Models for stationary processes: derivation of properties. Box-Jenkins approach to forecasting: model identification, estimation, verification. Forecasting using ARIMA and structural models. Forecast assessment. State space models and Kalman Filter. Comparison of procedures. Practical aspects of forecasting. Case studies in forecasting.
Key Texts - Available from .
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
听