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Deep Haar Scattering Networks in Unidimensional Pattern Recognition Problems

Author

Listed:
  • Fernando Fernandes Neto
  • Claudio Garcia, Rodrigo de Losso da Silveira Bueno, Pedro Delano Cavalcanti, Alemayehu Solomon Admas

Abstract

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.

Suggested Citation

  • Fernando Fernandes Neto & Claudio Garcia, Rodrigo de Losso da Silveira Bueno, Pedro Delano Cavalcanti, Alemayehu Solomon Admas, 2019. "Deep Haar Scattering Networks in Unidimensional Pattern Recognition Problems," Working Papers, Department of Economics 2019_16, University of São Paulo (FEA-USP).
    • Handle: RePEc:spa:wpaper:2019wpecon16
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    File URL: http://www.repec.eae.fea.usp.br/documentos/FNeto_Garcia_DeLosso_Cavalcanti_Admasu_16WPa.pdf
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    More about this item

    Keywords

    Haar Scattering Network; Pattern Recognition; Classification; Regression; Time Series.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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