BUS338 Forecasting

Credits (ECTS):10

Course responsible:Daumantas Bloznelis

Campus / Online:Taught campus Ås

Teaching language:Engelsk

Limits of class size:70

Course frequency:Annually

Nominal workload:250 hours.

Teaching and exam period:This course starts in Autumn parallel. This course has teaching/evaluation in Autumn parallel.

About this course

This course is a broad introduction into forecasting. It employs statistical models, decision rules and programming skills to address selected business problems. The course discusses types of forecasting problems and forecastability as well as types of forecasts (point, interval, density) and their optimality under different loss functions. It introduces a number of forecasting methods ranging from simple exponential smoothing to advanced automated algorithms and presents best practices such as forecast averaging. Forecast evaluation and comparison coupled with identification of superior forecasts are also covered.

Our purpose is to acquire practical forecasting skills based on a sound understanding of statistical and decision-theoretic principles and stylized facts of business data. Teaching combines lectures, exercise sessions and independent group work on mandatory assignments.

Learning outcome

Knowledge:

Students have advanced knowledge of

  • core ideas in forecasting
  • optimization criteria that underlie decision making
  • randomness and its manifestations, interpretations and role in forecasting

Skills:

Students can

  • formulate a forecasting problem mathematically
  • apply a variety of forecasting techniques
  • evaluate forecast performance
  • compare alternative forecasts and select between them
  • improve forecasts based on historical forecast errors and losses

General Competence:

Students

  • can analyze and discuss real-world forecasting problems
  • can evaluate forecasting solutions and foresee potential failures
  • can use R or other forecasting software
  • appreciate randomness and the human proclivity for mistaking noise for signal
  • Learning activities
    Lectures, exercises, group work, independent work.
  • Teaching support
    Office hours by appointment.
  • Prerequisites

    STAT100 Statistics or equivalent

    Basic knowledge of programing and data processing such as BUS350 Introduction to Data Analytics or INF120 Programming and Data Processing or equivalent

  • Recommended prerequisites
    Statistics and econometrics on bachelor level (e.g. ECN201 or STAT200).
  • Assessment method

    3,5 hour written exam counts for 100%.

    B1: Calculator handed out, no other aids



    School exam Karakterregel: Letter grades Hjelpemiddel: B1 Calculator handed out, no other aids
  • Examiner scheme
    External examiner will control quality of the syllabus, questions for the final examination, and principles for the assessment of the examination answers.
  • Mandatory activity

    Two mandatory group assignments. Both must be approved before the student can take the exam.

    Approved mandatory activities from the last time the course was held, is valid when retaking the course.

  • Teaching hours

    4 hours of teaching per week:

    2 times 2 hours of lectures.

  • Preferential right
    Master of Business Administration, major in Business Analytics.
  • Reduction of credits
    None.
  • Admission requirements
    The course is intended for students enrolled in master programmes at NMBU. It is also open for exchange students and other students with sufficient prior knowledge.