A stochastic model based on neural networks

Luciana C.D. Campos, Marley M.B.R. Vellasco, Juan G.L. Lazo

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations


This paper presents the proposal of a generic model of stochastic process based on neural networks, called Neural Stochastic Process (NSP). The proposed model can be applied to problems involving phenomena of stochastic behavior and / or periodic features. Through the NSP's neural networks it is possible to capture the historical series' behavior of these phenomena without requiring any a priori information about the series, as well as to generate synthetic time series with the same probabilities as the historical series. The NSP was applied to the treatment of monthly inflows series and the results indicate that the generated synthetic series exhibit statistical characteristics similar to historical series.
Original languageEnglish
Number of pages7
StatePublished - 24 Oct 2011
Externally publishedYes
EventProceedings of the International Joint Conference on Neural Networks -
Duration: 24 Oct 2011 → …


ConferenceProceedings of the International Joint Conference on Neural Networks
Period24/10/11 → …


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