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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

Cristian Rodrìguez Rivero
  • Corresponding author
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
/ Juliàn Pucheta
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
/ Sergio Laboret
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
/ Vìctor Sauchelli
  • Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba, Argentina
/ Daniel Patiǹo
  • Advanced Intelligent Systems Laboratory, Institute of Automatic Universidad Nacional de San JuanSan Juan, Argentina
Published Online: 2016-12-17 | DOI: https://doi.org/10.1515/jaiscr-2017-0001

Abstract

This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations.

Keywords: short time series; forecasting; missing data; energy associated to series; complete and incomplete datasets

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About the article

Published Online: 2016-12-17

Published in Print: 2017-01-01



Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2017-0001. Export Citation

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

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