  We present results for long term and intermediate term prediction algorithms
applied to a simple mechanical model of a fault. We use long term prediction
methods based, for example, on the distribution of repeat times between large
events to establish a benchmark for predictability in the model. In comparison,
intermediate term prediction techniques, analogous to the pattern recognition
algorithms CN and M8 introduced and studied by Keilis-Borok et al., are more
effective at predicting coming large events. We consider the implications of
several different quality functions Q which can be used to optimize the
algorithms with respect to features such as space, time, and magnitude windows,
and find that our results are not overly sensitive to variations in these
algorithm parameters. We also study the intrinsic uncertainties associated with
seismicity catalogs of restricted lengths.
