Learning in graphical models
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Markov chain Monte Carlo and dynamic programming for event sequence data
Knowledge and Information Systems
Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
Probabilistic analysis of a large-scale urban traffic sensor data set
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Predicting encounters in opportunistic networks
Proceedings of the 1st ACM workshop on High performance mobile opportunistic systems
Expediting search trend detection via prediction of query counts
Proceedings of the sixth ACM international conference on Web search and data mining
| Hi-index | 0.00 |
Time-series of count data occur in many different contexts, including Internet navigation logs, freeway traffic monitoring, and security logs associated with buildings. 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.