Introduction to Forecasting Models

  • Duration:
  • Spring semester , Exchange student
  • ECTS 7,5 ECTS
  • Application period Application deadline: 15 October

Introduction to Forecasting Models (ØKA2014) is a 7,5 ECTS course in the spring semester on Bachelor level. A 2-day home exam finalizes the course. The course is only open to exchange students from our partner institutions. 

To view possible course combinations, view "About the course".

About the course

Possible course combinations

Please be aware that this is a new course. We will check if the courses can be combined with the following courses. Information will be updated. 


Course time schedules

The course time schedule will be published after:

  • 01 December for the following spring semester


Recommended prerequisites:

  • Course in statistics
  • Interest in quantitative methods


Course material

To view the course material for ØKA2014 Introduction to Forecasting Models, please search  here. Do notice that the overview might not be available until a few weeks before the official semester start.


Course content

The topics covered are:

  • Forecasting concepts
  • Probability and statistics
  • Criteria for successful forecasts
  • Graphic analysis and descriptive statistics
  • Modelling trends, seasons, cycles
  • ARIMA models
  • Regression models
  • Evaluating forecasts
  • Combining forecasts
  • Co-integration models
  • Volatility models


Learning outcomes


Upon completion of the course, the candidate shall:

  • know the key concepts of forecasting and its application in economics, business, and finance.
  • know about the various time series components and suggest various techniques that can be used to model them properly.
  • be able to justify the use of various models or techniques based on model assumptions or other selected criteria.
  • explain the main assumptions of regression-based techniques in forecasting and discuss potential consequences if these are violated.
  • have knowledge of selected research on forecasting applications and performance within business/economics/finance.


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

  • collect business/financial/economic data from a range of data sources and subsequently organize and prepare them for further analysis.
  • use graphical tools and descriptive statistics to explore data properties.
  • apply selected techniques on real-life data using appropriate software.
  • construct forecasts by combing forecasting models.
  • build forecast models for volatility and correlation.
  • critically evaluate the performance of the various techniques using appropriate accuracy measures.

General Competence

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

  • conduct own research project using the data and techniques covered in the course.
  • summarize the key findings in the research project in an academic report and be able to discuss them with peers.


Teaching and working methods

The following teaching methods are used:

  • Lectures
  • Computer labs with guidance
  • Assignments and case studies
  • Termpaper writing and presentation with guidance


Coursework requirements

  • Two practical forecasting assignments.
  • Term paper and oral presentation of key results.



  • 48 hours individual take home exam. All resources (including software and resources available on the web) are allowed during the examination.
  • Grading is done on an A-F basis.

Practical information


On-campus housing is eligible for students applying for a full semester/academic year.

General information on accommodation at Campus Lillehammer.

What does it cost?

Free for exchange students

Study start information

An Orientation Week is arranged in the first week of the spring semester. Most lectures are expected to begin the same week.

Would you like to apply?

The course is available only for students coming from our partner institutions. Contact the International Coordinator at your home institution to find out if you are eligible for exchange studies at Inland Norway University of Applied Sciences (INN University). Please notice that applications can be submitted for one campus only.

Application procedure and documents