Determining the predictability of signals

In case of signal or time series prediction, it is important to know if there is any chance for prediction or not. Therefore, the maximum achievable prediction gain is the desired measure to characterize the future knowledge of a signal. In this paper, we present a method to evaluate the maximum prediction gain based on the observed signal only. Hence, the presented method does not rely on a special prediction function, therefore it is suitable for a decision whether any given predictor is good enough or could be improved. To aid system identification tasks the progress of the prediction gain is used as additional model selection rule. Considering different signal types the predictability behaves differently, i.e., it keeps constant for periodic signals or vanishes in case of chaotic or random signals.