  We explored the challenge of predicting and explaining the occurrence of
events within sequences of data points. Our focus was particularly on scenarios
in which unknown triggers causing the occurrence of events may consist of
non-consecutive, masked, noisy data points. This scenario is akin to an agent
tasked with learning to predict and explain the occurrence of events without
understanding the underlying processes or having access to crucial information.
Such scenarios are encountered across various fields, such as genomics,
hardware and software verification, and financial time series prediction. We
combined analytical, simulation, and machine learning (ML) approaches to
investigate, quantify, and provide solutions to this challenge. We deduced and
validated equations generally applicable to any variation of the underlying
challenge. Using these equations, we (1) described how the level of complexity
changes with various parameters (e.g., number of apparent and hidden states,
trigger length, confidence, etc.) and (2) quantified the data needed to
successfully train an ML model. We then (3) proved our ML solution learns and
subsequently identifies unknown triggers and predicts the occurrence of events.
If the complexity of the challenge is too high, our ML solution can identify
trigger candidates to be used to interactively probe the system under
investigation to determine the true trigger in a way considerably more
efficient than brute force methods. By sharing our findings, we aim to assist
others grappling with similar challenges, enabling estimates on the complexity
of their problem, the data required and a solution to solve it.
