The module will introduce students to time series models and associated forecasting methods.
Aims and Objectives
Having successfully completed this module you will be able to:
- Appreciate both the capabilities and the limitations of such computer-based techniques
- Formulate time series models and construct Python-based versions.
- Use Python functions built in various libraries to fit and analyse such models to data
- Produce well-structure assignment reports describing problem formulation and solution
Time Series Models: Decomposition, Analysis and Removal of Trends and Seasonality
Exponential Smoothing Methods: Single Exponential, Holt and Holt-Winters Methods
Simple and Multiple Regression Techniques
Box-Jenkins Methods for ARIMA models
Learning and Teaching
Teaching and learning methods
Lectures and computer workshops.
|Total study time||75|
Resources & Reading list
Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998). Forecasting: Methods and Applications. New York: Wiley.
Phuong Vothihong, Martin Czygan, Ivan Idris, Magnus Vilhelm Persson & Luiz Felipe Martins (2017). Python: End to End Data Analysis. Packt Publishing.
Anderson, R.A., Sweeney, D.J. and Williams, T.A. (1994). An Introduction to Management Science. West Publishing Co.
Wetherill, GB. (1981). Intermediate Statistical Methods. London: Chapman and Hall.
Rob J Hyndman and George Athanasopoulos (2012). Forecasting: principles and practice. Ortexts.com.
Gilchrist, W.G. (1976). Statistical Forecasting. New York: Wiley.
Draper, N.R. and Smith, H. (1981). Applied Regression Abalysis. New York: John Wiley.
Janert, P.K. (2011). Data Analysis with Open Source Tools. Sebastopol: O'Reilly.
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.
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Repeat type: Internal & External