  We have developed a novel prediction method based on string invariants. The
method does not require learning but a small set of parameters must be set to
achieve optimal performance. We have implemented an evolutionary algorithm for
the parametric optimization. We have tested the performance of the method on
artificial and real world data and compared the performance to statistical
methods and to a number of artificial intelligence methods. We have used data
and the results of a prediction competition as a benchmark. The results show
that the method performs well in single step prediction but the methods
performance for multiple step prediction needs to be improved. The method works
well for a wide range of parameters.
