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CVPR
1999
IEEE
14 years 9 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
ESWS
2008
Springer
13 years 9 months ago
Creating and Using Geospatial Ontology Time Series in a Semantic Cultural Heritage Portal
Content annotations in semantic cultural heritage portals commonly make spatiotemporal references to historical regions and places using names whose meanings are different in diffe...
Tomi Kauppinen, Jari Väätäinen, Eer...
CCS
2006
ACM
13 years 11 months ago
Time series modeling for IDS alert management
Intrusion detection systems create large amounts of alerts. Significant part of these alerts can be seen as background noise of an operational information system, and its quantity...
Jouni Viinikka, Hervé Debar, Ludovic M&eacu...
ESWA
2007
96views more  ESWA 2007»
13 years 7 months ago
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Forecasting airborne pollen concentrations is one of the most studied topics in aerobiology, due to its crucial application to allergology. The most used tools for this problem ar...
José Luis Aznarte, José Manuel Benit...
SDM
2009
SIAM
291views Data Mining» more  SDM 2009»
14 years 4 months ago
Detection and Characterization of Anomalies in Multivariate Time Series.
Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other d...
Christopher Potter, Haibin Cheng, Pang-Ning Tan, S...