Finding Periodic Discrete Events in Noisy Streams

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.