Event Forecasting with Pattern Markov Chains
This work addresses event forecasting for users needing to detect patterns in streams, but it appears incremental as it builds on existing Pattern Markov Chains for a specific application.
The paper tackles the problem of online probabilistic event forecasting for detecting and forecasting event patterns given as regular expressions from streams, using Pattern Markov Chains to learn a probabilistic model that produces forecasts with guaranteed precision in the form of intervals. Experimental results on real-world datasets show the quality of forecasts explored using precision scores, spread (interval length), and distance (how early forecasts are reported).
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.