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Singular spectrum analysis (SSA) is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals. Leaving out the residual components and adding up the others, the time series can be smoothed. The scenarios generations through PAR(p), periodic autoregressive models, has been broadly used in modeling Affluent natural Energy series. This article presents an approach of this decomposition method, in which the PAR (p) model is applied to the smoothed series obtained by SSA and its multivariate version, named MSSA. In this case, 200 scenarios are generated for a forecasting horizon of 60 months. To illustrate the application of the proposed methodology were considered wind speed time series recorded at two locations in northeastern Brazil. The results show that both the PAR (p) model and scenarios generations are favored by prior smoothing of the time series by SSA / MSSA.

GERAÇÃO DE CENÁRIOS DE VELOCIDADE DO VENTO COM MODELAGEM AUTORREGRESSIVA PERIÓDICA ASSOCIADA À MSSA