UE Statistical methods for forecasting

Degrees incorporating this pedagocial element :

Description

This course is related to mathematical and statistical methods for forecasting in supervised learning. We will present several tools and ingredients to predict the future value of a variable. We shall focus on methods for regression and methods for classification for inde- pendent or correlated training dataset. This course will be followed by four practical sessions with the R software.

Course outline: Introduction. Linear methods for regression. Non linear methods for regression. Supervised classification.

Syllabus

Parametric regression, Lasso, Ridge, Nonparametric trend estimation, Kernel nonparametric models, Smoothing parameter selection, Average squared error, Mean average squared error, Mallows criterion, Cross validation, Generalized cross validation, Dependent random variables, Martingale difference sequences, Stochastic Volatility, Moment inequalities, Maximal inequali- ties, Supervised classification.