title: Unorganized machines for seasonal streamflow series forecasting.
abstract: Modern unorganized machines--extreme learning machines and echo state networks--provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.
authors.affiliation: Department of Systems Engineering, School of Electrical and Computer Engineering, University of Campinas, Av. Albert Einstein, 400, Campinas, São Paulo 13083-852, Brazil.
authors.forename: Hugo
authors.initials: H
authors.lastname: Siqueira
authors.forename: Levy
authors.initials: L
authors.lastname: Boccato
authors.forename: Romis
authors.initials: R
authors.lastname: Attux
authors.forename: Christiano
authors.initials: C
authors.lastname: Lyra
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005544:Forecasting; D006801:Humans; D016571:Neural Networks, Computer; D012621:Seasons