Finding Periodic Discrete Events in Noisy Streams

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Periodic phenomena are ubiquitous, but detecting and predicting periodic events can be difficult in noisy environments. We describe a model of periodic events that covers both idealized and realistic scenarios characterized by multiple kinds of noise. Thee 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 €filter that can efficiently and accurately estimate the parameters of the process generating periodic events intermingled with independent noise events. Thee system has a small memory footprint, and, unlike alternative methods, its computational complexity is constant in the number of events that have been observed. As a result, it can be applied in low-resource settings that require real-time performance over long periods of time. In experiments on real and simulated data we €find that it outperforms existing methods in accuracy and can track changes in periodicity and other characteristics in dynamic event streams.
Original languageEnglish
Title of host publicationThe 26th ACM Conference on Information and Knowledge Management (CIKM) 2017
Number of pages10
ISBN (Electronic)9781450349185
Publication statusPublished - 6 Nov 2017
Event2017 ACM Conference on Information and Knowledge Management - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017


Conference2017 ACM Conference on Information and Knowledge Management
Abbreviated titleCIKM 2017
Internet address


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