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Forecasting I

  • Teacher(s): M.Boldi , D.Flores Agreda
  • Course given in: English
  • ECTS Credits: 3 credits
  • Schedule: Spring Semester 2018-2019, 2.0h. course (weekly average)
  •  séances
  • site web du cours course website
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Objectives

This lecture covers some of the main forecasting methods for chronological data using the Time Series Analysis approach.

The course will cover the following topics:

  1. Introduction to Time Series Data
  2. Tools for Exploratory Analysis
  3. Regression-based Methods
  4. Time Series Decomposition
  5. Exponential Smoothing
  6. ARIMA Models
  7. Elements of Dynamic Time Series Modelling

Contents

Upon success, the candidate will be able to:

  • Recognize patterns in Time Series data
  • Use appropriate methods for their analysis.
  • Produce informative graphics and reports either for analytical purposes or presentation.
  • Use the R package forecast (Hyndman, 2017) while understanding the underlying methods.

References

Most of the content will be based on the book by Hyndman & Athanasopoulos (2014), available online at http://otexts.org/fpp2.

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

One written exam will be organized during the semester: 100% of the grade.

Retake

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

Retake exam: 100% of the grade



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