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<H1><A NAME="SECTION00050000000000000000">Nonlinear prediction</A></H1>
<A NAME="secpredict"> </A>
To think about predictability in time series data is worth while even if one
is not interested in forecasts at all. Predictability is one way how
correlations between data express themselves. These can be linear
correlations, nonlinear correlations, or even deterministic contraints.
Questions related to those relevant for predictions will reappear with noise
reduction and in surrogate data tests, but also for the computation of
Lyapunov exponents from data. Prediction is discussed in most of the general
nonlinear time series references, in particular, a nice collection of articles
can be found in [<A HREF="citation.html#SFI" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/chaospaper/citation.html#SFI">17</A>].
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<LI> <A NAME="tex2html233" HREF="node18.html#SECTION00052000000000000000" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/chaospaper/node18.html#SECTION00052000000000000000">Simple nonlinear prediction</A>
<LI> <A NAME="tex2html234" HREF="node19.html#SECTION00053000000000000000" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/chaospaper/node19.html#SECTION00053000000000000000">Finding unstable periodic orbits</A>
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<P><ADDRESS>
<I>Thomas Schreiber <BR>
Wed Jan 6 15:38:27 CET 1999</I>
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