MØLBA3004 Business Forecasting

    • Number of credits
      7,5
    • Teaching semester
      2026 Spring
    • Language of instruction
      English
    • Campus
      Lillehammer
    • Required prerequisite knowledge

      Recommended prerequisites: MØLBA3001 Data Engineering and LDBA200 Applied Programming.

Course content

The topics covered are: 

  • Time series data patterns and decomposition 
  • Moving averages and exponential smoothing methods 
  • Dynamic regression models 
  • ARIMA models 
  • Multivariate time series models (e.g., vector autoregression) 
  • Advanced forecasting methods (e.g., Neural network and State Space models) 
  • Forecast combinations  
  • Loss functions, forecast accuracy and forecast optimality 

Learning Outcome

Knowledge

Upon completion of the course, the candidate shall: 

  • Have advanced knowledge of time-series data patterns and how to decompose them into trend, cycles, season, and random noise (k1) 
  • Have specialized knowledge of advanced time series models commonly used in business forecasting (k2) 
  • Explain and exemplify how forecasts generated from various methods can be compared and ranked based on a range of accuracy measures (k3) 
  • Demonstrate limitations of time series models and (lack of) forecast robustness under unstable conditions (k4) 
  • Have in-depth knowledge of the key findings from recent research on forecast accuracy of different models applied to business or economic data (k5) 
Skills

Upon completion of the course, the candidate shall be able to: 

  • Decompose time series data into structural components using appropriate techniques (f1) 
  • Generate time series forecasts using the techniques covered in the course (f2) 
  • Combine individual forecasts to improve accuracy (f3) 
  • Evaluate forecast accuracy using a range of accuracy measures (f4)  
General competence

Upon completion of the course, the candidate shall be able to: 

  • Plan and manage forecasting projects which involve the topics covered in the course (g1) 
  • Recommend forecasting techniques that are suited in a range of business applications (g2) 
  • Recognize limitations of forecasting models and anticipate conditions under which they break (g3) 
Teaching and working methods

The following teaching methods are used: 

  • Lectures 
  • Problem solving sessions 
  • Tutorial videos 
  • Case studies 
  • Quizzes 
Required coursework
  • Mandatory homework assignments must be handed in before each teaching module (a total of 4). These will be combinations of practical and theoretical exercises covering key topics in the course. 
  • Three out of four homework assignments must be passed to be allowed to take the exam.  
  • Attendance on at least 50% of the courses lectured teaching.
Form of assessment

Four-hour individual school exam under attendance. 

Graded A-F, where E is minimum for passing the exam.

Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Written examination with invigilation
ECTS - A-F
Individual
4 Hour(s)
  • Approved calculator
100%

Reading list

Faculty
Inland School of Business and Social Sciences
Department
Department of Business Administration