This module will provide an introduction to time series models in common use and their use for predicting future observations and/or estimating unobservable components like trend and seasonal effects.
Aims and Objectives
Having successfully completed this module you will be able to:
- Decompose a time series into trend, seasonal and irregular components
- Determine how and when to apply different methods of time series analysis and how to test for goodness of fit using the software package X12
- Understand and be able to apply the concepts and methods underlying the analysis of univariate time series, and the context for interpretation of results
- Understand the theoretical bases of different methods of time series analysis including decomposition
- Difference of time series data compared to other data sets (equidistant observations, calendar effects, outliers)
- Basic concepts of time series: Stationarity, Ergodicity, Autocorrelations, Partial Autocorrelations
- Global models for trends and seasonals
- The periodogram and spectral analysis
- Local models and moving average methods
- ARIMA modelling and forecasting
- Exponential smoothing
- Estimation of unobservable components using a software package (X12ARIMA)
Learning and Teaching
Teaching and learning methods
Depending on feasibility, teaching may be delivered face to face intensively over a week, or online using a mixture of synchronous and asynchronous online methods, which may include lectures, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study.
|Total study time||100|
Resources & Reading list
Laboratory space and equipment required. Practical computing lab in X12ARIMA
Harvey, A.C. (1993). Time Series Models.
Harvey, A.C. (1989). Forecasting Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Wei, W. S. (1994). Time Series Analysis: Univariate and Multivariate Methods. Addison-Wesley Publishing company.
Chatfield, C. (1996). The Analysis of Time Series: An Introduction. Chapman & Hall.
There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one online assignment.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External