The University of Southampton
Courses

MANG2065 Business Forecasting

Module Overview

This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year. Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. This module gives you a thorough understanding of various statistical methods for forecasting, in particular time-series methods that have wide applications in business. Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts, and sometimes it is necessary to provide distributional rather than point forecasts. As such, an introduction to methods for distributional forecasting will also be provided. As forecasting often requires huge amount of data, both for training and testing the models, and the required formulae and equations are often complicated, it is essential to implement forecasting methods using a proper statistical package. As such training will be provided on using SAS package for implementing forecasting methods.

Aims and Objectives

Module Aims

This module aims to introduce the student to time series models and associated forecasting methods, and show them how such models and methods can be implemented in SAS package.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Different fields of application of time series analysis and forecasting;
  • The capabilities as well as limitations of quantitative-based forecasting methods;
  • The importance of incorporating uncertainty in forecasting.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Self-manage the development of learning and study skills;
  • Plan and control effectively for successful completion of a personal workload;
  • Communicate effectively, in both oral and written form, using and justifying argument within reports and presentations.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Formulate time series models including exponential smoothing methods, ARIMA methods, and innovations state space models;
  • Use SAS to fit and analyse such models to data;
  • Choose the most appropriate forecasting method using various types of information criterion.

Syllabus

The topics covered in this module will include: • Introduction to Forecasting: quantitative and qualitative methods; • Time series models: decomposition, analysis and removal of trend, seasonality, and cycle; • Exponential Smoothing Methods: Single Exponential, Holt and Holt-Winters Methods; • Box-Jenkins Methods for ARIMA models; • Simple and Multiple Regression Techniques; • Introduction to Innovations State Space models.

Learning and Teaching

Teaching and learning methods

Teaching methods include • Lectures • Interactive case studies • Problem-solving activities • Computer labs • Directed reading • Private/guided study. Learning activities include • Introductory lectures • An assignment (individual written coursework) • Case study / problem solving activities • In class debate and discussion • Private study • Use of video and online materials

TypeHours
Preparation for scheduled sessions20
Follow-up work16
Completion of assessment task70
Lecture24
Supervised time in studio/workshop12
Total study time142

Resources & Reading list

Hyndman, R.J. and Athanasopoulos, G (2013). Forecasting: principles and practice. 

Hyndman R.J., Koehler, A.B., Keith Ord, J. and Snyder, R. D (2008). Forecasting with Exponential Smoothing: The State Space Approach. 

Assessment

Formative

Lab work

Summative

MethodPercentage contribution
Individual assignment  (2500 words) 100%

Repeat

MethodPercentage contribution
Individual assignment  (2500 words) 100%

Referral

MethodPercentage contribution
Individual assignment  (2500 words) 100%
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