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In this article we describe a framework for detecting anomalous events in such data using an unsupervised learning approach. Normal periodic behavior is modeled via a time-varying Poisson process model, which in turn is modulated by a hidden Markov process that accounts for bursty events. We outline a Bayesian framework for learning the parameters of this model from count time-series. Two large real-world datasets of time-series counts are used as testbeds to validate the approach, consisting of freeway traffic data and logs of people entering and exiting a building. We show that the proposed model is significantly more accurate at detecting known events than a more traditional threshold-based technique. We also describe how the model can be used to investigate different degrees of periodicity in the data, including systematic day-of-week and time-of-day effects, and to make inferences about different aspects of events such as number of vehicles or people involved. The results indicate that the Markov-modulated Poisson framework provides a robust and accurate framework for adaptively and autonomously learning how to separate unusual bursty events from traces of normal human activity.<\/jats:p>","DOI":"10.1145\/1297332.1297337","type":"journal-article","created":{"date-parts":[[2007,12,7]],"date-time":"2007-12-07T19:19:01Z","timestamp":1197055141000},"page":"13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["Learning to detect events with Markov-modulated poisson processes"],"prefix":"10.1145","volume":"1","author":[{"given":"Alexander","family":"Ihler","sequence":"first","affiliation":[{"name":"University of California, Irvine, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jon","family":"Hutchins","sequence":"additional","affiliation":[{"name":"University of California, Irvine, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Padhraic","family":"Smyth","sequence":"additional","affiliation":[{"name":"University of California, Irvine, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2007,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177697196"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.62"},{"volume-title":"the 80th Annual Meeting of the Transportation Research Board. 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Scott, S. L. and Smyth, P. 2003. The Markov modulated Poisson process and Markov Poisson cascade with applications to Web traffic data. In Bayesian Statistics, vol. 7, M. J. Bayarri et al., eds. 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