2024-2025 / SDOC0032-1

Analysis of time series

Duration

20h Th

Number of credits

 Doctoral training in sciences (BMCB)3 crédits 

Lecturer

Samuel Nicolay

Language(s) of instruction

French language

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The aim of time series analysis is to study variables over time. Time series data are utilized in fields such as astronomy, meteorology, econometrics, and financial mathematics. Various types of data analysis methods are available for time series, each suited to different purposes.

General exploration

  • Graphical examination of data series
  • Autocorrelation analysis
  • Spectral analysis
Description

  • Separation into components representing trend, seasonality, slow and fast variation
  • Simple properties of marginal distribution
Prediction and forecasting

  • Fully formed statistical models for stochastic simulation purposes.
  • Simple or fully formed statistical models to describe the likely outcome of the time series.

Learning outcomes of the learning unit

The aim of the course is to provide the tools that are necessary to perform basic time series analysis.
The student should be able to solve autonomously typical problems of the time series analysis.

Prerequisite knowledge and skills

General mathematic course

Planned learning activities and teaching methods

The exercises are supervised by the teaching assistants, allowing students to apply the concepts taught during the course in practice.

Mode of delivery (face to face, distance learning, hybrid learning)

Blended learning


Further information:

The course is delivered either according to the official schedule distributed to students at the beginning of the academic year, online, or in a hybrid format. The teaching method will be agreed upon at the start of the year.

Course materials and recommended or required readings

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )

- Remote

written exam ( open-ended questions )


Further information:

The exam consists in solving problems with a computer.

Work placement(s)

Organisational remarks and main changes to the course

Contacts

S. Nicolay Analyse mathématique Institut de Mathématique - Grande Traverse, 12 - Sart Tilman -Bât. B 37 - 4000 LIEGE 1 email: S.Nicolay@ulg.ac.be

Association of one or more MOOCs

Items online

Course notes
Notes will be given at the beginningof the course.