This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm. The EPST learns a model online based on the statistics of an event based input and can make predictions over multiple overlapping patterns. The EPST uses a representation specific to event based data, defined as a portion of the power set of event subsequences within a short context window. It is explainable, and possesses many promising properties such as fault tolerance, resistance to event noise, as well as the capability for one-shot learning. The computational features of the EPST are examined in a synthetic data prediction task with additive event noise, event jitter, and dropout. The resulting algorithm outputs predicted projections for the near term future of the signal, which may be applied to tasks such as event based anomaly detection or pattern recognition.

翻译：本文介绍了基于事件的预测后缀树（EPST），这是一种受生物启发的、基于事件的预测算法。EPST基于事件输入的统计信息在线学习模型，能够对多个重叠模式进行预测。该算法采用针对事件数据的特定表示形式，即短上下文窗口内事件子序列幂集的一部分。EPST具有可解释性，并具备许多有前景的特性，如容错性、抗事件噪声能力以及单样本学习能力。通过在包含加性事件噪声、事件抖动和丢失事件的合成数据预测任务中，评估了EPST的计算特征。该算法输出信号近期的预测投影，可应用于基于事件的异常检测或模式识别等任务。