Discovery of Precursors to Adverse Events using Time Series Data.

We develop an algorithm for automatic discovery of precursors in time series data (ADOPT). In a time series setting, a precursor may be considered as any event that precedes and increases the likelihood of an adverse event. In a multivariate time series data, there are exponential number of events which makes a brute force search intractable. ADOPT works by breaking down the problem into two steps - (1) inferring a model of the nominal time series (data without adverse event) by considering the nominal data to be generated by a hidden expert and (2) using the expert's model as a benchmark to evaluate the adverse time series to identify suboptimal events as precursors. For step (1), we use a Markov Decision Process (MDP) framework where value functions and Bellman's optimality are used to infer the expert's actions. For step (2), we define a precursor score to evaluate a given instant of a time series by comparing its utility with that of the expert. Thus, the search for precursors is transformed to a search for sub-optimal action sequences in ADOPT. As an application case study, we use ADOPT to discover precursors to go-around events in commercial flights using real aviation data.