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Periodic phenomena are ubiquitous, but detecting and predicting periodic events can be di cult in noisy environments. We describe a model of periodic events that covers both idealized and realistic scenarios characterized by multiple kinds of noise. e model incorporates false-positive events and the possibility that the underlying period and phase of the events change over time. We then describe a particle lter that can e ciently and accurately estimate the parameters of the process generatingdoi:10.1145/3132847.3132981 dblp:conf/cikm/GhoshLS17 fatcat:q7y7io3eajgehc7fcto7misk5q