ETF5231 - Business forecasting - 2018

6 points, SCA Band 2, 0.125 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.


Business and Economics

Organisational Unit

Department of Econometrics and Business Statistics

Chief examiner(s)

Associate Professor George Athanasopoulos


Associate Professor George Athanasopoulos

Unit guides



  • First semester 2018 (On-campus)


Students must have passed ETX2111, ETF2100, ETX2121Not offered in 2018, ETF2121, ETF9100, ETF5910, ETF9121, ETF5912, ETF9300, ETF5930, MBA9007, MBA5907 or equivalent.


ETX3231Not offered in 2018, ETC2450Not offered in 2018, ETF3231


Forecasts are crucial for guiding the planning and decision making process in business. In this unit you will learn to apply reliable methods for generating accurate forecasts in a rapidly changing business environment. These include: an introduction to regression modelling from a forecasting perspective, classical decomposition, exponential smoothing, Box-Jenkins ARIMA modelling and judgemental forecasting. You will build your programming skills by learning to program in R, a free programming language for statistical computing.


The learning goals associated with this unit are to:

  1. develop statistical skills for analysing data in a business environment
  2. learn how to build accurate and robust models for forecasting
  3. acquire computer skills vital for forecasting business and economic data
  4. gain knowledge of the theoretical aspects of the models used.


Within semester assessment: 40% + Examination: 60%

Workload requirements

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

See also Unit timetable information