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This work addresses the problem of forecasting and generation series monthly average streamflows. It is noteworthy that the importance of forecasting future values of the series of monthly streamflows as well as the generation of synthetic series are fundamental for planning the operation of Brazilian hydroelectric systems. These series have a periodic behavior on average, variance and autocorrelation function and therefore it is considered for standard series periodic autoregressive models PAR(pm). At the forecast classical analysis of the prediction error is made in function of the prediction horizon. In this study, the forecasting errors are calculated in the original scale of the series of streamflow, depending on the model parameters adjusted and evaluated for forecasting horizons h ranging from 1 to 12 months. These errors are compared with estimates of the variances of the streamflows for the month is provided. Regarding the bayesian prediction, we adopt the models Normal, Log-Normal and t-Student in estimation procedures and, then, is a study of the performance of these models using the mean square error, mean absolute error and mean absolute percentage error. In relation to generation, a Log-Normal multivariate model with three parameters and a Log-Normal generalized model were developed and analyzed using the Kullback-Leibler criterion. As a result there has been an assessment of the predictive power, in months, the adjusted models for each month, the choice of the

Modelos autorregressivos periódicos para previsão e geração de séries de vazões médias mensais