Times series

Teacher(s)

Kiriliouk Anna;

Language

English

Prerequisites

Concepts and tools equivalent to those taught in teaching units

LDATS2030	Programmation et data reporting en R
LSTAT2120	Linear models

Main themes

Introduction to time series focusing on modelling, estimation and prediction of two types of processes: linear processes and non-linear heteroscedastic models. The approach will be essentially parametric—students will learn how to quantify statistical uncertainty in estimating the parameters of the stochastic model for the observed series, with the prediction of future values of this series as the main objective.

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. Introduction to State Space models

Teaching methods

Classroom lectures and R tutorials.

Evaluation methods

Students are assessed in two ways:

• A compulsory assignment (an R-based computer project) must be submitted at the end of the semester and will be followed by a brief oral defence during the examination period. The project and its defence will together account for 50% of the final grade.
• A written examination during the examination period, covering all course material, will account for 50% of the final grade.

In accordance with Article 72 of the General Regulations for Studies and Examinations, the course instructor may recommend to the examination board that a student who has not submitted the required work by the specified deadline be refused registration for the examination.

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