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A dimensionality reduction process to forecast events through stochastic models

Abstract : This paper describes a dimensionality reduction process to forecast time series events using stochastic models. As well as the KDD process defines a sequence of common steps to achieve useful information through data mining techniques, we propose a sequence of steps in order to estimate the probability of future events through stochastic modeling. Our process focus on reduce the dimension-ality of data, thus reducing the effect of the common problems involved in stochastic modeling, such as the state space explosion and the large modeling efforts to create such models.
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https://hal.inria.fr/hal-01247905
Contributor : Arnaud Legrand <>
Submitted on : Wednesday, December 23, 2015 - 9:42:24 AM
Last modification on : Tuesday, May 11, 2021 - 11:36:25 AM
Long-term archiving on: : Sunday, April 30, 2017 - 12:11:50 AM

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Joaquim Assunção, Paulo Fernandes, Lucelene Lopes, Silvio Normey. A dimensionality reduction process to forecast events through stochastic models. International Conference on Software Engineering and Knowledge Engineering, Jul 2015, Pittsburgh, United States. ⟨hal-01247905⟩

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