We face two basic problems when trying to use ML systems on prediction tasks. First the variable being predicted in a time series is usually real valued. Most ML systems are not able to deal with numeric classes. Secondly, the time series model assumes that there exists some form of correlation between successive values of the variable being predicted. Assuming that this is relevant information we need to enable ML systems to learn theories that somehow relate class values to previous class values.