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

Abstract: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$\unicode{x2013}$a pattern we find common in event data recorded from real-world processes.

Machine Learning (cs.LG)

arXiv:2507.21616 [cs.LG]

(or arXiv:2507.21616v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2507.21616 Focus to learn more arXiv-issued DOI via DataCite

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