6533b873fe1ef96bd12d4dfa
RESEARCH PRODUCT
Modelling Recurrent Events for Improving Online Change Detection
Mykola PechenizkiyTommi KärkkäinenAlexandr V. MaslovIndre ZliobaiteIndre Zliobaitesubject
ta113noiseComputer scienceData stream miningMessage passingDetectordata streamsonline change detection02 engineering and technologycomputer.software_genreTask (computing)recurrent eventschange points020204 information systems0202 electrical engineering electronic engineering information engineeringProbability distribution020201 artificial intelligence & image processingNoise (video)Data miningBaseline (configuration management)computerChange detectiondescription
The task of online change point detection in sensor data streams is often complicated due to presence of noise that can be mistaken for real changes and therefore affecting performance of change detectors. Most of the existing change detection methods assume that changes are independent from each other and occur at random in time. In this paper we study how performance of detectors can be improved in case of recurrent changes. We analytically demonstrate under which conditions and for how long recurrence information is useful for improving the detection accuracy. We propose a simple computationally efficient message passing procedure for calculating a predictive probability distribution of change occurrence in the future. We demonstrate two straightforward ways to apply the proposed procedure to existing change detection algorithms. Our experimental analysis illustrates the effectiveness of these approaches in improving the performance of a baseline online change detector by incorporating recurrence information.Read More: http://epubs.siam.org/doi/10.1137/1.9781611974348.62
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-06-30 | Proceedings of the 2016 SIAM International Conference on Data Mining |