Duration
20h Th, 10h Pr
Number of credits
| Master in geography, global change, research focus | 3 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The aim of the time series analysis is to studying variables over time. Time series are used in astronomy, meteorology, econometry and financial mathematics. There are several types of data analysis available for time series. The purpose is to master the basic tools for analyzing climatic time series.
Learning outcomes of the learning unit
The aim of the course is to provide students with the tools required for the study and analysis of time series, with particular emphasis on applications in climatology.
By the end of the course, students should be able to:
- understand and apply basic time series analysis methods;
- independently solve standard problems in time series analysis, particularly in the context of climatology;
- analyse and interpret the results obtained using these methods;
- use Scilab independently for the processing, analysis, and graphical representation of time series.
Prerequisite knowledge and skills
General mathematic course, general statistic course.
Planned learning activities and teaching methods
Teaching is primarily based on students' independent work. Basic texts on time series and their implementation using Matlab are made available to students, together with exercises designed to help them put the concepts and methods studied into practice.
Students are expected to study the different topics covered in the course using these materials, solve the proposed exercises independently, and explore in greater depth a selected part of the material as part of the work prepared for the final assessment.
The instructor supports students throughout their learning process by providing additional explanations, answering questions, and helping them address difficulties encountered, particularly through individual meetings or electronic communication.
This approach aims to develop students' independence and rigour, as well as their ability to solve time series problems and implement the methods studied using Matlab.
Mode of delivery (face to face, distance learning, hybrid learning)
Remote course
Further information:
The course is taught remotely during the first semester. Students are expected to work independently, using the resources provided to them. The instructor remains available throughout the semester to answer questions, provide additional explanations, and support students throughout their learning process.
Course materials and recommended or required readings
Written work / report
Continuous assessment
Further information:
The assessment consists of:
- a project to be submitted, involving the solution of a concrete problem using Scilab, which accounts for 70% of the final grade;
- continuous assessment based on the work carried out throughout the course, which accounts for 30% of the final grade.
Work placement(s)
Organisational remarks and main changes to the course
The course is taught remotely during the first semester. Students are expected to work primarily independently, with guidance and support from the instructor throughout the semester. Communication and supervision are conducted remotely, mainly by electronic means and, when necessary, through individual meetings.
Contacts
S. Nicolay Analyse mathématique Institut de Mathématique - Grande Traverse, 12 - Sart Tilman -Bât. B 37 - 4000 LIEGE 1 email: S.Nicolay@uliege.be