# Event Forecasting with Pattern Markov Chains

arXiv: https://arxiv.org/abs/1804.10388 · HTML: https://mlsanity.com/paper/1804.10388/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report](https://mlsanity.com/paper/2109.00287/) (2021)
- [Symbolic Register Automata for Complex Event Recognition and Forecasting](https://mlsanity.com/paper/2110.04032/) (2021)
- [Information Cascade Prediction under Public Emergencies: A Survey](https://mlsanity.com/paper/2404.01319/) (2024)

## Abstract (arXiv)

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.