A NN-based model for time series forecasting in function of energy associated of series

  • Authors:

  • C. Rodríguez Rivero;J. Pucheta;J. Baumgartner;M. Herrera;D. Patiño;B. Kuchen

  • Affiliations:

  • Departments of Electrical and Electronic Engineering, Mathematics Research Laboratory applied to Control, Faculty of Exact, Physical and Natural Sciences, National University of Córdoba, C ...;Departments of Electrical and Electronic Engineering, Mathematics Research Laboratory applied to Control, Faculty of Exact, Physical and Natural Sciences, National University of Córdoba, C ...;Departments of Electrical and Electronic Engineering, Mathematics Research Laboratory applied to Control, Faculty of Exact, Physical and Natural Sciences, National University of Córdoba, C ...;Departments of Electrical Engineering, at Faculty of Sciences and Applied Technologies, National University of Catamarca, Catamarca, Argentina;Institute of Automatics, Faculty of Engineering, National University of San Juan, San Juan, Argentina;Institute of Automatics, Faculty of Engineering, National University of San Juan, San Juan, Argentina

  • Venue:
  • ICANCM'11/ICDCC'11 Proceedings of the 2011 international conference on applied, numerical and computational mathematics, and Proceedings of the 2011 international conference on Computers, digital communications and computing
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this work an algorithm to adjust parameters of time series forecasting in function of energy associated of series using a feed-forward NN-based nonlinear autoregressive model is presented. The criterion for fitting comprises to yield value time series from forecasted time series area. These values are approximated by the NN to generate a primitive calculated as an area by the predictor filter. The NN output will tend to approximate the current value available from the series which has the same Hurst Parameter as the real time series. The approach is tested over a time series obtained from samples of the Mackey-Glass delay differential equations (MG) and serve to be applied for meteorological variables measurements such as soil moisture series, daily rainfall and monthly cumulative rainfall time series forecasting.