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Time Series and Forecasting

  • Enseignant(s):
  • Titre en français: Séries temporelles et prévision
  • Cours donné en: anglais
  • Crédits ECTS:
  • Horaire: Semestre de printemps 2018-2019, 2.0h. de cours (moyenne hebdomadaire)
      WARNING :   this is an old version of the syllabus, old versions contain   OBSOLETE   data.
  •  séances
  • site web du cours site web du cours
  • Formations concernées:

 

Objectifs

This lecture aims to give an overview of the main forecasting methods for Time Series data. Special emphasis will be given to the assessment of the issues arising in this type of analysis and the review of computational tools designed to tackle them.

At the end of the lecture, the candidate will be able to:

  • Recognize patterns in Time Series data and 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.

Contenus

The course will cover the following topics:

  1. Introduction to Time Series Data
  2. Tools for Exploratory Analysis
  3. A Reminder of Regression
  4. Time Series Decomposition
  5. Exponential Smoothing
  6. ARIMA Models
  7. Dynamic Time Series Modelling
  8. Judgmental Forecasting

Références

Most of the content will be based on the book by Hyndman & Athanasopoulos (2014), available online at http://otexts.org/fpp2. The interested reader might also find useful information in Shumway & Stoffer (2006) and Cryer & Chan (2008).

  • Cryer, Jonathan D, & Chan, Kung-Sik. 2008. Time series regression models. Time series analysis: with applications in R, 249–276.
  • Hyndman, Rob J. 2017. forecast: Forecasting functions for time series and linear models. R package version 8.2.
  • Hyndman, Rob J, & Athanasopoulos, George. 2014. Forecasting: principles and practice. OTexts.
  • Shumway, Robert H, & Stoffer, David S. 2006. Time series analysis and its applications: with R examples. Springer Science & Business Media.

Pré-requis

Familiarity with the R environment and basic mathematical background.

Knoweldge of Probability and some notions of Statistical inference (e.g. Hypothesis testing, etc.) could be an advantage, but not compulsory.

Evaluation

1ère tentative

Examen:
Ecrit 2h00 heures
Documentation:
Autorisée
Calculatrice:
Autorisée
Evaluation:

The examination will consist on:

  • Data Analysis: A concise written report on the analysis of a previously provided dataset.
  • Written Exam: An 2h written exam with questions on the analyzed data, some theoretical questions and the assessment of the analysis of another data set.

Each of these items account for half of the final mark.

Rattrapage

Examen:
Ecrit 2h00 heures
Documentation:
Autorisée
Calculatrice:
Autorisée
Evaluation:

Same modalities. That said, the mark of the Data Analysis part on the ordinary session exam will be kept, unless it is deemed insufficient.

In the latter case, the report shall be rewritten by the candidate.



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