Resumo

BAUTISTA - THOMPSON, E; GUZMAN - RAMIREZ, E  e  FIGUEROA - NAZUNO, J. Forecasting of Multiple Points in Time Series with Support Vector Machines. Comp. y Sist. [online]. 2004, vol.7, n.3, pp.148-155. ISSN 2007-9737.

This paper presents the evaluation of the forecasting for multiple points in time series, by means of Support Vector Machines (SVM) with a shifting window and two different kernel functions (linear and radial basis). The evaluation was made with a set of 30 time series from different origins and dynamics. The results show that SVM has a good capability for the adaptation to different time series dynamics, and also presents a good performance for the forecasting of the first points of the time series using the radial basis kernel function, in spite of the expansion of the forecast error.

Palavras-chave : Kernel Functions for SVM; Time Series Forecasting; Support Vector Machines.

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