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Forecasting I

  • Teacher(s):   M.Wilhelm  
  • Course given in: English
  • ECTS Credits: 3 credits
  • Schedule: Spring Semester 2019-2020, 2.0h. course (weekly average)
  •  sessions
  • site web du cours course website
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Objectives

Upon success, the candidate should be able to:

  • recognize usual patterns in time series data;
  • use appropriate methods for their analysis (or be able to acknowledge the inadequacy of the known methods);
  • produce informative graphics and reports either for analytical purposes or presentation;
  • recognize strengths and weaknesses of some forecasts.

Contents

This lecture covers some of the main forecasting methods for chronological data.

The course will cover the following topics:

  1. Introduction to time series data
  2. AR(I)MA models
  3. Exponential smoothing
  4. Elements of dynamic time series modelling

References

  • Brockwell, P. and Davis, R.Introduction to Time Series Modelling and Forecasting, 2nd edition, Springer, New-York (2002).
  • Hyndman, R. J. and Athanasopoulos, G. Forecasting: principles and practice, 3rd edition, OTexts, Melbourne (2019).

Pre-requisites

There are no official prerequisites for the lecture, but it is assumed that the students enrolled have the following background:

  • basic knowledge of R language
  • probability and statistics
  • calculus (Bachelor HEC)

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

One written exam will be organized during the semester.

Retake

Exam:
Written 2h00 hours
Documentation:
Allowed
Calculator:
Allowed
Evaluation:

Retake exam: 100% of the grade



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