  We present a system for online probabilistic event forecasting. We assume
that a user is interested in detecting and forecasting event patterns, given in
the form of regular expressions. Our system can consume streams of events and
forecast when the pattern is expected to be fully matched. As more events are
consumed, the system revises its forecasts to reflect possible changes in the
state of the pattern. The framework of Pattern Markov Chains is used in order
to learn a probabilistic model for the pattern, with which forecasts with
guaranteed precision may be produced, in the form of intervals within which a
full match is expected. Experimental results from real-world datasets are shown
and the quality of the produced forecasts is explored, using both precision
scores and two other metrics: spread, which refers to the "focusing resolution"
of a forecast (interval length), and distance, which captures how early a
forecast is reported.
