A dimensionality reduction process to forecast events through stochastic models

Joaquim Assunção 1 Paulo Fernandes 2 Lucelene Lopes 2 Silvio Normey 2
1 Université Grenoble Alpes [Saint Martin d'Hères]
2 PUCRS - Pontifícia Universidade Católica do Rio Grande do Sul [Porto Alegre]
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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Communication dans un congrès
International Conference on Software Engineering and Knowledge Engineering, Jul 2015, Pittsburgh, United States. 2015, 〈http://www.ksi.edu/seke/seke15.html〉
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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. 2015, 〈http://www.ksi.edu/seke/seke15.html〉. 〈hal-01247905〉

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