Edge Selection for the Effective use of Piecewise-Constant Distributions as Neural Network Outputs for Event Prediction

We study the output representation of a neural network used for next event prediction. We propose partitioning the time axis into a fixed set of intervals and having a neural network output a categorical distribution over them, which we map to a (mostly) piecewise-constant probability density. We present an optimization procedure that selects interval edges in order to maximize data likelihood under the representation. The representation is well suited to processes whose inter-event distribution is a mixture of smooth and sharply peaked components—a pattern we find common in event data recorded from real-world processes.