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xxx9 Business Forecasting



Upon completion of the course, the student can: 

  • discuss the importance and potential business value of accurate forecasts within a range of industries, 
  • describe the key findings from recent research on the accuracy of various forecasting techniques and combinations of techniques, 
  • identify various time series components from visual inspection and descriptive and modelling outputs, 
  • describe how the various forecasting techniques generate predictions and how accuracy can be assessed and 
  • review the most important findings from research within some topics of the course. 


Upon completion of the course, the student can: 

  • analyse time series data and its various components using the techniques covered in the course, 
  • evaluate the performance of various forecasting techniques by calculating a range of accuracy measures, 
  • classify categorical outcomes, 
  • evaluate the classification accuracy using confusion matrix, 
  • perform data reduction using principal component analysis, 
  • estimate forecasts using machine learning techniques and 
  • estimate and calculate forecasts based on combinations of individual techniques. 

General competence 

Upon completion of the course, the student can: 

  • plan and manage forecasting projects which involve the topics covered in the course, 
  • recommend forecasting techniques that are suited to a range of business applications and 
  • report general findings from the comparison of forecasting techniques on specific business problems.


The following topics are covered. 

  • The business value of forecasting 
  • Time series components (trend, seasonality, cycles and random movements) 
  • Loss functions and forecast accuracy 
  • Moving averages, exponential smoothing and regression-based forecasting methods 
  • Causal forecasting methods 
  • Principal component analysis 
  • Statistical-/ machine learning methods 
  • Forecast combinations and forecast optimality

Arbeids- og undervisningsformer

The pedagogic approach is fully interactive and illustrated with relevant data from business, economics and finance. The focus will be more on the empirical implementation of the techniques than on their theoretical underpinnings. The techniques will be illustrated with several empirical applications, and then implemented using state-of-the-art software. 

The following teaching methods are used. 

  • Lectures 
  • Problem solving sessions 
  • Tutorial videos 
  • Case studies 
  • Quizzes

Obligatoriske krav som må være godkjent før eksamen kan avlegges

  • Students must pass three out of four mandatory course requirements to be allowed to take the exam. 
  • 50% attendance in classes is required.


  • 48-hour individual home exam.

Tillatte hjelpemidler til eksamen

  • All resources.