Times series

lstat2170  2023-2024  Louvain-la-Neuve

Times series
5.00 credits
30.0 h + 7.5 h
Concepts and tools equivalent to those taught in teaching units
LSTAT2020Logiciels et programmation statistique de base
LSTAT2120Linear models
Main themes
The principal subjects of this course on an introduction into time series analysis will include the modelling, estimation and prediction of two types of processes - linear processes and heteroscedastic models of non-linear processes. We follow basically a parametric approach - the student will learn how to quantify statistical uncertainty while estimating the model parameters for the problem of forecasting future values of the observedseries.
Learning outcomes

At the end of this learning unit, the student is able to :

1 The aim of this course is to give a good comprehension of the theory and application of stochastic time series modelling, with a view towards prediction (forecasting).
1. Modelling time series data: an introduction
2. Linear processes - simple parametric models (ARMA)
3. Estimation and prediction of ARMA models
4. Box-Jenkins analysis - (S)ARIMA models
5. Non-linear processes - heteroscedastic (G)ARCH models - applications to modelling financial data
6. Extensions to multivariate (bivariate) series
Teaching methods
Basic models of linear time series will be treated in the first part. The data analysis, i.e. estimation of the model parameters for forecasting, will be based predominantly on Box-Jenkins methods. In the second part of the course some elements of modelling financial data with the more recently developed ARCH and GARCH models will be given and included into the practical part of the course (done with the R-software). Some extensions for treating multivariate (bivariate) time series finalise the course.
Evaluation methods
The examination will be oral. An applied data analysis project has to be prepared on the computer.
Other information
Prerequisites A general knowledge of basic statistical concepts (on the level of a first introductory course in statistics) is necessary.
Online resources
Brockwell, P. and R. Davis (1996), Introduction to Time Series and Forecasting. Springer, New York
Brockwell, P and R. Davis (1991), Time Series, Theory and Methods. Springer, New York
Gourieroux, Ch. (1992), Modèles ARCH et applications financières. Economica, Paris
Teaching materials
  • Transparents sur moodle
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Data Science : Statistic

Master [120] in Biomedical Engineering

Master [120] in Statistics: Biostatistics

Master [120] in Actuarial Science

Master [120] in Statistics: General

Master [120] in Mathematical Engineering

Master [120] in Economics: General

Certificat d'université : Statistique et science des données (15/30 crédits)