MATH6011 Forecasting

Module Overview

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

Aims and Objectives

Module Aims

- introduce the student to time series models and associated forecasting methods; - show how such models and methods can be implemented using corresponding Python libraries to analyse time series data; - give an appreciation of the different fields of application of time series analysis and forecasting; - convey the value of such quantitatively based methods for solving realistic practical problems.

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
Independent Study	58
Teaching	16
Total study time	74

Resources & Reading list

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

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

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

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.

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

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

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

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