Forecasting

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Formulate time series models and construct Python-based versions.
• Use Python functions built in various libraries to fit and analyse such models to data
• Appreciate both the capabilities and the limitations of such computer-based techniques
• 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

Teaching and learning methods

Lectures and computer workshops.

Resources & Reading list

Textbooks

Anderson, R.A., Sweeney, D.J. and Williams, T.A. (1994). An Introduction to Management Science. West Publishing Co.

Rob J Hyndman and George Athanasopoulos (2012). Forecasting: principles and practice. Ortexts.com.

Phuong Vothihong, Martin Czygan, Ivan Idris, Magnus Vilhelm Persson & Luiz Felipe Martins (2017). Python: End to End Data Analysis. Packt Publishing.

Gilchrist, W.G. (1976). Statistical Forecasting. New York: Wiley.

Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998). Forecasting: Methods and Applications. New York: Wiley.

Wetherill, GB. (1981). Intermediate Statistical Methods. London: Chapman and Hall.

Janert, P.K. (2011). Data Analysis with Open Source Tools. Sebastopol: O'Reilly.

Draper, N.R. and Smith, H. (1981). Applied Regression Abalysis. New York: John Wiley.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

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 Information

Repeat type: Internal & External