The practical interest of using ensemble methods has been highlighted in several works. Sequential prediction provides a natural framework for adapting ensemble methods to time series data. Main developments focus on the rules of aggregation of a set of experts and examine how to weight and combine the experts. However, very few work exist regarding how to choose/generate the experts to include in a given aggregation procedure. We use the concept of diversity to propose some strategies to enrich the set of original individual predictors. We show how this method is connected to recent theoretical work on boosting. We propose a simulation study to illustrate the interest of our approach in the regression setting. An application on real datasets (electricity consumption and pollution data) shows the potentiality of this method for practical forecasting tasks.

Jairo Cugliari ¹
Universite Lumiere Lyon ²

Jueves 05 de Marzo
Hora 14:00
Instituto de Estadística – IESTA – FCEA – UdelaR
Eduardo Acevedo 1139