A Model for Temporal Dependencies in Event Streams

Asela Gunawardana, Christopher Meek, and Puyang Xu

Abstract

We introduce the Piecewise-Constant Conditional Intensity Model, a model for learning temporal dependencies in event streams. We describe a closed-form Bayesian approach to learning these models, and describe an importance sampling algorithm for forecasting future events using these models, using a proposal distribution based on Poisson superposition. We then use synthetic data, supercomputer event logs, and web search query logs to illustrate that our learning algorithm can efficiently learn nonlinear temporal dependencies, and that our importance sampling algorithm can effectively forecast future events.

Details

Publication typeInproceedings
Published inNeural Information Processing Systems
PublisherNeural Information Processing Systems Foundation
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