Identifying Precursors to Anomalies Using Inverse Reinforcement Learning

Shared by Vijay Manikandan Janakiraman, updated on Mar 28, 2016


Author(s) :
Vijay Manikandan Janakiraman, Santanu Das, Bryan Matthews, Nikunj Oza

In this paper, we consider the problem of discovering candidate precursors to anomalies in a set of time sequenced data. Typical scenarios involving time sequential data include dynamical systems and general monitoring systems. In such scenarios, a precursor could be any event that frequently precedes a given event of interest. Anomalies are rare but significant events in time series data and identifying precursors to anomalies is vital in proactive management. In this work, an inverse reinforcement learning (IRL) based method is formulated to succinctly represent the nominal behavior and identify sequences that preceded the anomalous events. A preliminary evaluation is performed on flight recorded data identifying challenges and future directions for application.

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Publication Name
Workshop on Optimization Methods for Anomaly Detection, SDM 2014
Publication Location
Philadelphia, Pennsylvania
Year Published


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