Artículos de revistas
Nonstationary regression with support vector machines
Fecha
2014-10-07Registro en:
Uzal, Lucas César; Grinblat, Guillermo Luis; Granitto, Pablo Miguel; Verdes, Pablo Fabian; Nonstationary regression with support vector machines; Springer; Neural Computing And Applications; 26; 3; 7-10-2014; 641-649
0941-0643
Autor
Uzal, Lucas César
Grinblat, Guillermo Luis
Granitto, Pablo Miguel
Verdes, Pablo Fabian
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 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.