  We discuss the development of a Java toolbox for astronomical time series
data. Rather than using methods conventional in astronomy (e.g., power spectrum
and cross-correlation analysis) we employ rule discovery techniques commonly
used in analyzing stock-market data. By clustering patterns found within the
data, rule discovery allows one to build predictive models, allowing one to
forecast when a given event might occur or whether the occurrence of one event
will trigger a second. We have tested the toolbox and accompanying display tool
on datasets (representing several classes of objects) from the RXTE All Sky
Monitor. We use these datasets to illustrate the methods and functionality of
the toolbox. We also discuss issues that can come up in data analysis as well
as the possible future development of the package.
