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An Adaptive Neural System for Financial Time Series Tracking

  • A. C. H. Dantas
  • J. M. Seixas
Conference paper

Abstract

In this paper, we present a neural network based system to generate an adaptive model for financial time series tracking. This kind of data is quite relevant for data quality monitoring in large databases. The proposed system uses the past samples of the series to indicate its future trend and to generate a corridor inside which the future samples should lie. This corridor is derived from an adaptive forecasting model, which makes use of the walk-forward method to take into account the most recent observations of the series and bring up to date the values of the neural model parameters. The model can serve also to manage other time series characteristics, such as the detection of irregularities.

Keywords

Time Series Data Time Series Forecast Financial Time Series Future Sample Data Quality Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • A. C. H. Dantas
    • 1
  • J. M. Seixas
    • 1
  1. 1.Signal Processing LabCOPPE/EP - Federal University of Rio de JaneiroRio de JaneiroBrazil

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