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Suppose we are given a time series or a signal $x(t)$ for $0\leq t\leq T$. We consider the problem of predicting the signal in the interval $T<t\leq T+t_{f}$ based on a knowledge of its history and nothing more. We ask the following question: what is the largest value of $t_{f}$ for which a prediction can be made? We show that the answer to this question is contained in a fundamental result of information theory due to Wyner, Ziv, Ornstein, and Weiss. In particular, for the class of chaotic signals, the upper bound is $t_{f}\leq\log_{2}T/H$ in the limit $T\rightarrow\infty$, with $H$ being entropy in a sense that is explained in the text. If $\bigl|x(T-s)-x(t^{\ast}-s)\bigr|$ is small for $0\leq s\leq\tau$, where $\tau$ is of the order of a characteristic time scale, the pattern of events leading up to $t=T$ is similar to the pattern of events leading up to $t=t^{\ast}$. It is reasonable to expect $x(t^{\ast}+t_{f})$ to be a good predictor of $x(T+t_{f})$. All existing methods for prediction use this idea...

Metric Entropy and the Optimal Prediction of Chaotic Signals