Duration
20h Th
Number of credits
| Doctoral training in sciences (BMCB) | 3 crédits |
Lecturer
Language(s) of instruction
French language
Schedule
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
- Separation into components representing trend, seasonality, slow and fast variation
- Simple properties of marginal distribution
- 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
Additional information:
The course will be given following the timetable available at the beginning of the academic year
Recommended or required readings
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
- Remote
written exam ( open-ended questions )
Additional information:
The exam consists in solving problems given at the beginning of the exam.
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.