Applying Propositional Learning to Time Series Prediction
Luís Torgo
1995
Abstract
In this paper we present some work on trying to
apply propositional learning systems to the problem of time series
prediction. We describe the difficulties of trying to adapt
classification systems to prediction problems. Some techniques to
perform this adaptation are presented. These techniques are based on
the introduction of new attributes that convey relevant information
about the temporal behavior of a time series. These strategies
are evaluated on some time series problems. Results indicate that the
predictive performance strongly depends on the used set of attributes
and that the best set is domain-dependent. Automatic strategies for
finding the optimal set of attributes are sketched out. Other
experiments were carried out in order to compare our performance to
the one obtained by other types of methods in the same data sets. The
results compare reasonably to other methods, although indicating that
more work needs to be done before machine learning-based systems can
compete with other techniques on prediction problems.