Nonstationary regression with support vector machines

GRINBLAT, GUILLERMO L.; UZAL, LUCAS C.; VERDES PABLO F.; GRANITTO P. M.

NEURAL COMPUTING AND APPLICATIONS

SPRINGER

Lugar: Berlin; Año: 2014 p. 1 - 9

0941-0643

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 that 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.