• J. Pucheta
  • D. Patiño
  • B. Kuchen
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)


In this work an adaptive linear filter model in a autoregressive moving average (ARMA) topology for forecasting time series is presented. The time series are composed by observations of the accumulative rainfall every month during several years. The learning rule used to adjust the filter coefficients is mainly based on the gradient-descendent method. In function of the long and short term stochastic dependence of the time series, we propose an on-line heuristic law to set the training process and to modify the filter topology. The input patterns for the predictor filter are the values of the time series after applying a time-delay operator. Hence, the filter’s output will tend to approximate the current value available from the data series. The approach is tested over a time series obtained from measures of the monthly accumulative rainfall from La Perla, Cordoba, Argentina. The performance of the presented approach is shown by forecasting the following 18 months from a hypothetical actual time for four time series of 102 data length.


Fractional Brownian Motion Forecast Time Series Hurst Parameter Rainfall Time Series Rainfall Forecast 
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 Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.LIMAC, Departments of Electronic Engineering and Electrical EngineeringNational University of CórdobaCórdoba
  2. 2.Institute of Automatics, Faculty of Engineering, National University of San JuanSan Juan

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