The University of Southampton
Courses

# MATH6011 Forecasting

## Module Overview

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

### Aims and Objectives

#### 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

### 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.

TypeHours
Teaching16
Independent Study58
Total study time74

#### 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

MethodPercentage contribution
Coursework 100%

#### Repeat

MethodPercentage contribution
Coursework 100%

#### Referral

MethodPercentage contribution
Coursework 100%

#### Repeat Information

Repeat type: Internal & External