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This work presents the performance analysis of nonlinear autoregressive neural networks applied in server traffic prediction and dedicated to hybrid intra-data center networks. This technique offers a high potential for accurate forecasting of heavy traffic streams, which allows for an a priori scheduling and allocation of optical switching circuits for offloading of intensive flows in data center applications. Just as with the artificial data sets carrying the machine-generated traffic characteristics used in our previous work, the nonlinear autoregressive neural networks show a high performance of the prediction mechanism applied to real server traffic.