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This work proposes using DBSCAN (Density Based Spatial Clustering of Applications with Noise) to separate the noise components of eigentriples in the grouping stage of the Singular Spectrum Analysis (SSA) of Time Series. DBSCAN is a modern and expert method at identify noise through regions of lower density. This work shows better efficiency of DBSCAN over the others methods already used in this stage of SSA, because it allows considerable reduction of noise and provides better forecasting. The result is supported by experimental evaluations realized for simulated series of Box-Jenkins models and the approach is applied at a real time series of speed wind.

COMBINAÇÃO DE CLUSTERIZAÇÃO BASEADA EM DENSIDADE COM ANÁLISE ESPECTRAL SINGULAR DE SÉRIES TEMPORAIS - UMA APLICAÇÃO A VELOCIDADE DO VENTO