Programming in R, Stochastic Processes and Multivariate Analysis.

The main objectives of this course on functional data analysis can be summarized as follows: 1. Understand the fundamental concepts and techniques of functional data analysis, including mathematical tools, smoothing methods, and handling functional random variables using real data examples. 2. Master the application of Functional Principal Component Analysis (PCA) for inference, analyzing sample characteristics, assessing depths in functional data, and detecting outliers in functional datasets. 3. Gain proficiency in Functional Linear Regression, encompassing the solution of the functional regression problem, handling scalar-on-function regression, and addressing function-on-function regression scenarios. 4. Learn the classification techniques with functional data, starting with an introduction, proceeding to unsupervised classification methods, and advancing to supervised classification approaches. 5. Explore the intricacies of Functional Time Series analysis, focusing on estimation and prediction utilizing functional principal components. Through these objectives, the course aims to equip students with a comprehensive understanding of functional data analysis, enabling them to apply these techniques to real-world data and solve complex problems in various domains.

1. Introduction to Functional Data Analysis. 2. Exploratory Functional Data Analysis. 3. Functional Regression. 4. Classification of Functional Data. 5. Functional Time Series.

LEARNING ACTIVITIES RELATED TO MATTERS AF1 Theoretical class AF2 Practical class AF4 Laboratory AF5 Tutoring AF6 Group work AF7 Individual work AF8 In-person evaluation tests Code activity Total hours In-person hours % In-person hours - student AF1 44 44 100 AF2 20 20 100 AF4 20 20 100 AF5 16 16 100 AF6 40 0 0 AF7 154 0 0 AF8 6 6 100 TOTAL MATTER 300 100 33 TEACHING METHODOLOGIES RELATED TO MATTERS MD1 Explanations in the theoretical class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students learning. MD3 Resolution of case study, problems, etc. raised by the professor individually or in groups MD5 Development of projects and reports individually or in groups