Forecasting

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Appreciate both the capabilities and the limitations of such computer-based techniques
• 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
• Formulate time series models and construct Python-based versions.

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

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

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

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

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

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

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.

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

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