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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...
CVPR
2007
IEEE
14 years 9 months ago
On the Blind Classification of Time Series
We propose a cord distance in the space of dynamical models that takes into account their dynamics, including transients, output maps and input distributions. In data analysis app...
Alessandro Bissacco, Stefano Soatto
IWANN
2005
Springer
14 years 1 months ago
Input Selection for Long-Term Prediction of Time Series
Prediction of time series is an important problem in many areas of science and engineering. Extending the horizon of predictions further to the future is the challenging and diffic...
Jarkko Tikka, Jaakko Hollmén, Amaury Lendas...
KDD
2012
ACM
257views Data Mining» more  KDD 2012»
11 years 10 months ago
Aggregating web offers to determine product prices
Historical prices are important information that can help consumers decide whether the time is right to buy a product. They provide both a context to the users, and facilitate the...
Rakesh Agrawal, Samuel Ieong
NN
2010
Springer
225views Neural Networks» more  NN 2010»
13 years 6 months ago
Learning to imitate stochastic time series in a compositional way by chaos
This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing ...
Jun Namikawa, Jun Tani