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Forecasting II

  • Teacher(s):   V.Chavez  
  • Course given in: English
  • ECTS Credits: 3 credits
  • Schedule: Spring Semester 2020-2021, 3.0h. course (weekly average)
  •  sessions
  • site web du cours course website
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Objectives

This lecture is the practical part related to Forecasting 1. The students, organized in groups, will analyze time series data applying Forecasting 1 techniques (visualisation, decomposition, ETS, ARIMA, etc.). The emphasis will be on

  • Data representation and exploratory analysis
  • Modelling strategy (model choice)
  • Relevant forecasting
  • Forecast evaluation

Contents

Upon success, the student will be able to

  • Select and test several methods to analyze and forecast time series dat.
  • Use R to make exploratory analysis (graphs and tables), to make statistical modeling (model fit and inference) and to forecast time series.
  • Produce and present a report of the analysis.

References

Brockwell, P. and Davis, R. (2002). Introduction to Time Series Modelling and Forcasting, 2nd edition, Springer, New-York

Hyndman R. J. & Athanasopoulos, G. (2014). Forcasting: principles and practice, 2nd edition, OTexts, Melbourne. Available online at http://otexts.org/fpp2

Pre-requisites

THIS COURSE CAN ONLY BE TAKEN IN PARALLEL TO FORECASTING 1.

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

Each group have to provide a written report on the detail of their analysis before the deadline, in addition to the presentation during the last session of the course. The two will make the note of the group as follow:

Group note = 0.5 * Report + 0.4 * Presentation + 0.1 * Continuous evaluation

Then the final note of each student depend on individual behavior score during the project as follow:

Final note= 0.6 * Group note + 0.4 * Participation

Due date : Week 14

Retake

Exam:
Without exam (cf. terms)  
Evaluation:

A complement to the report will be asked.

The final report is 100% of the grade.



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