Event Forecasting with Pattern Markov Chains

E. Alevizos, A. Artikis, G. Paliouras
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引用次数: 33

Abstract

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.
基于模式马尔可夫链的事件预测
提出了一个在线概率事件预测系统。我们假设用户对检测和预测事件模式感兴趣,事件模式以正则表达式的形式给出。我们的系统可以使用事件流,并预测模式何时被期望完全匹配。随着越来越多的事件被消耗,系统修改其预测以反映模式状态的可能变化。使用模式马尔可夫链框架来学习模式的概率模型,该模型可以以期望完全匹配的区间形式产生具有保证精度的预测。展示了来自真实世界数据集的实验结果,并使用精度分数和另外两个指标探索了生成的预测的质量:传播,它指的是预测的“聚焦分辨率”(间隔长度),以及距离,它捕获了预测报告的早期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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