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.
|Número de páginas
|Publicada - 24 oct. 2011
|Publicado de forma externa
|Proceedings of the International Joint Conference on Neural Networks -
Duración: 24 oct. 2011 → …
|Proceedings of the International Joint Conference on Neural Networks
|24/10/11 → …