MATH6011 Forecasting

Module Overview

The module will introduce students to time series models and associated forecasting methods.

Aims and Objectives

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

Syllabus

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.

Type	Hours
Teaching	16
Independent Study	58
Total study time	74

Resources & Reading list

Draper, N.R. and Smith, H. (1981). Applied Regression Abalysis.

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

Janert, P.K. (2011). Data Analysis with Open Source Tools.

Wetherill, GB. (1981). Intermediate Statistical Methods.

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

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

Gilchrist, W.G. (1976). Statistical Forecasting.

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

Assessment

Summative

Method	Percentage contribution
Coursework	100%

Repeat

Method	Percentage contribution
Coursework	100%

Referral

Method	Percentage contribution
Coursework	100%

Repeat Information

Repeat type: Internal & External