Competencies and objectives

 

Course context for academic year 2024-25

Time series is an optional subject. The objective of the course is to provide an introduction to an important class of univariate time series models:  the  class of AutoRegressive Integrated Moving Average (ARIMA) models.  Within this class,  we will cover the problems of model identification, estimation, selection and model diagnosis and prediction. For computation, visualization and analysis of time series data we will use the free software R. This course assumes knowledge of probability theory and statistical theory (at the level taught in the probability and statistical inference courses 25026 and 25035). Background on linear regression methods and familiarity with the statistical software R will be helpful but not requiered.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25

Specific Competences (CE)

  • CE10 : Communicate, both orally and in writing, mathematical knowledge, procedures, results and ideas.
  • CE11 : Ability to solve academic, technical, financial and social problems using mathematical methods.
  • CE12 : Ability to work in a team, providing mathematical models adapted to the needs of the group.
  • CE15 : Recognise and analyse new problems and prepare strategies to resolve them.
  • CE5 : Propose, analyse, validate and interpret models of simple real-life situations, using the most appropriate mathematical tools for the purpose.
  • CE6 : Solve mathematical problems using basic calculus skills and other techniques, planning their resolution according to the tools available and any time and resource restriction.
  • CE7 : Use computer applications for statistical analysis, numerical calculus and symbolic calculus, graphic visualisation and others to experiment in Mathematics and solve problems.
  • CE9 : Use bibliographic search tools for Mathematics.

 

Specific Generic UA Competences

  • CGUA1 : Understand scientific English.
  • CGUA2 : Possess computer skills relevant to the field of study.
  • CGUA3 : Acquire or possess basic Information and Communications Technology skills and correctly manage the information gathered.

 

Generic Degree Course Competences

  • CG1 : Develop the capacity for analysis, synthesis and critical reasoning.
  • CG2 : Show the capacity for effective and efficient management/direction: entrepreneurial spirit, initiative, creativity, organisation, planning, control, decision making and negotiation.
  • CG3 : Solve problems effectively.
  • CG4 : Show capacity for teamwork.
  • CG5 : Commitment to ethics, the values of equality and social responsibility as a citizen and professional.
  • CG6 : Self-learning.
  • CG7 : Show the capacity to adapt to new situations.
  • CG9 : Show the ability to transmit information, ideas, problems and solutions to both specialist and non-specialist audiences.

 

 

 

Learning outcomes (Training objectives)

No data

 

 

Specific objectives stated by the academic staff for academic year 2024-25

  • Learn how to build, estimate and validate ARIMA time series models for univariate time series data 
  • Learn how to use ARIMA models for time series forecasting
  • Utilize R for computation, visualization and analysis of time series data

 

 

General

Code: 25070
Lecturer responsible:
TROTTINI, MARIO
Credits ECTS: 6,00
Theoretical credits: 1,12
Practical credits: 1,28
Distance-base hours: 3,60

Departments involved

  • Dept: MATHEMATICS
    Area: STATISTICS AND OPERATIONS RESEARCH
    Theoretical credits: 1,12
    Practical credits: 1,28
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught