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In time series theory, the prediction of future values is a widely discussed subject. There are manyfold methods to derive models from data. One of the main objectives is to obtain the model parameters. Some proposals use self adapting techniques like Neural Networks to estimate the model parameters. Most of these approaches predict one future value of a time series. Some simulation tasks require models for traffic sources that are closely related to time series prediction though there exist different requirements. One of them is that a simulated traffic source should show the same stochastic behavior as a reference source. In this paper a procedure is presented that automatically adapts to a given reference source in the sense described above.