INVESTIGADORES
GRANITTO Pablo Miguel
artículos
Título:
Nonstationary regression with Support Vector Machines
Autor/es:
G.L. GRINBLAT; L.C. UZAL; P. F. VERDES; P. M. GRANITTO
Revista:
NEURAL COMPUTING AND APPLICATIONS
Editorial:
SPRINGER
Referencias:
Lugar: Berlin; Año: 2015 vol. 26 p. 641 - 649
ISSN:
0941-0643
Resumen:
In this work we introduce a method for data analysis in nonstationary environments: Time Adaptive Support Vector Regression (TA-SVR). The proposed approach extends a previous development which was limited to classification problems. Focusing our study on time series applications, we show that TA-SVR can improve the accuracy of several aspects of nonstationary data analysis, namely the tasks of modelling and prediction, input relevance estimation, and reconstruction of a hidden forcing profile.
