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Poster
A Model for Temporal Dependencies in Event Streams
Asela Gunawardana · Christopher Meek · Puyang Xu

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @ None #None

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.

Author Information

Asela "Zilla" Gunawardana (Microsoft Research)
Christopher Meek (Microsoft Research)
Puyang Xu (Johns Hopkins University)

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