Linear Models Applied to Monthly Seasonal Streamflow Series Prediction

Linear models are widely used to perform time series forecasting. The Autoregressive stand out, due their simplicity in the parameters adjustment based on close-form solution. and Moving Average (ARMA) Infinite Impulse Response filters (IIR) also good alternatives, since they recurrent structures. However, is more complex, problem has no analytical This investigation performs linear predict monthly seasonal streamflow series, from Brazilian hydroelectric plants. goal reach best achievable performance addressing approaches. We propose application of models, estimating via an immune algorithm. To compare optimization performance, Least Mean Square (LMS) Recursive Prediction Error (RPE) algorithms utilized. Also, AR model Holt-Winters method were performed. results showed that insertion feedback loops increases quality responses. ARMA optimized by achieved overall performance.