The main purpose of this study is to introduce Bayesian neural network approach for forecasting autoregressive moving average time series models. The proposed approach is compared with the classical Bayesian approach. To achieve these objectives first 48000 samples are generated from different autoregressive moving average models, different sample sizes (25, 50,100) were used for the network training and testing .Then accuracy of the proposed approach was evaluated and compared with the classical Bayesian method using three tools; the mean square error , the mean absolute deviation of error and mean absolute error ratio.Second both Bayesian neural network and the classical Bayesian approaches were used to forecast five real data series according to three cases: perfect prior, some prior and no prior .Two macro computer programs were designed (MATLAB code). The first for Bayesian neural network training, testing and comparing with Bayesian method, and the second for calculating automatically the proposed neural network forecasts. Bayesian analysis of autoregressive moving average models is difficult since the likelihood function is analytically intractable, which causes problems in prior specification and posterior analysis. The results showed that the performance of proposed Bayesian neural network approach for forecasting is better than the performance of Bayesian method.