Times series

Teacher(s)

Kiriliouk Anna;

Language

English

Prerequisites

Concepts and tools equivalent to those taught in teaching units

LSTAT2020	Logiciels et programmation statistique de base
LSTAT2120	Linear 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).

Content

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

Classroom lectures and R tutorials.

Evaluation methods

The exam consists of two parts:

• A compulsory project (in R) is to be submitted at the end of the semester and will count for 50% of the final grade. Homework assignments (to be submitted during the semester) will collectively account for 10% of the final grade. 
• An oral exam covering all course material (40% of the final grade). Questions about the assignment will also be part of the exam.

Following Article 72 of the General Regulations for Studies and Examinations, the course instructor may propose to the jury that a student who has not submitted the assignment in time is to be prohibited from registering for the exam.

Other information

Prerequisites A general knowledge of basic statistical concepts (on the level of a first introductory course in statistics) is necessary.

Online resources

https://moodle.uclouvain.be/course/view.php?id=1960

Bibliography

Brockwell, P. and R. Davis (2016), Introduction to Time Series and Forecasting (3rd edition). Springer.
Shumway & Stoffer (2019), Time series: a data analysis approach using R. CRC Press. 
Shumway & Stoffer (2025), Time series analysis and its applications: with R examples. Springer.
Cowpertwait & Metcalfe (2009). Introductory Time Series with R. Springer.

Teaching materials

• Transparents sur moodle

Faculty or entity

> LSBA