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Multiple Model Adaptive Estimation (MMAE) is a Bayesian technique that applies a bank of Kalman filters to predict future observations. Each Kalman filter is based on a different set of parameters and hence produces different residuals. The likelihood of each Kalman filter's prediction is determined by a magnitude of the residuals. Since some researchers have obtained good forecasts using a single Kalman filter, we tested MMAE's ability to make time series predictions. Our Kalman filters have a dynamics model based on a Box-Jenkins Auto-Regressive Moving Average (ARMA) model and a measure model with additive noise. The time-series prediction is based on the probabilistic weighted Kalman filter predictions. We make a probability interval about that estimate also based on the filter probabilities. In a Monte Carlo analysis, we test this MMAE approach and report the results based on many different criteria. Our analysis tests the robustness of the approach by testing its ability to make predictions when the Kalman filter dynamics models did not match the data generation time-series model. Our analysis indicates benefits in applying multiple model adaptive estimation for time series analysis.