Deep Haar Scattering Networks in Unidimensional Pattern Recognition Problems
Fernando Fernandes Neto and
Claudio Garcia, Rodrigo de Losso da Silveira Bueno, Pedro Delano Cavalcanti, Alemayehu Solomon Admas
Authors registered in the RePEc Author Service: Rodrigo De-Losso
No 2019_16, Working Papers, Department of Economics from University of São Paulo (FEA-USP)
The aim of this paper is to discuss the use of Haar scattering networks, which is a very simple architecture that naturally supports a large number of stacked layers, yet with very few parameters, in a relatively broad set of pattern recognition problems, including regression and classification tasks. This architecture, basically, consists of stacking convolutional filters, that can be thought as a generalization of Haar wavelets, followed by nonlinear operators which aim to extract symmetries and invariances that are later fed in a classification/regression algorithm. We show that good results can be obtained with the proposed method for both kind of tasks. We outperformed the best available algorithms in 4 out of 18 important data classification problems, and obtained a more robust performance than ARIMA and ETS time series methods in regression problems for data with invariances and symmetries, with desirable features, such as possibility to evaluate parameter stability and easy structural assessment.
Keywords: Haar Scattering Network; Pattern Recognition; Classification; Regression; Time Series. (search for similar items in EconPapers)
JEL-codes: C38 C45 C52 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-bec, nep-big, nep-cmp and nep-ore
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