<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

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

Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba,

Advanced Intelligent Systems Laboratory, Institute of Automatic Universidad Nacional de San JuanSan Juan,

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete...

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

Publié en ligne: 17 déc. 2016

Pages: 5 - 16

DOI: https://doi.org/10.1515/jaiscr-2017-0001

Mots clés
short time series, forecasting, missing data, energy associated to series, complete and incomplete datasets

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Publié en ligne: 17 déc. 2016

Pages: 5 - 16

DOI: https://doi.org/10.1515/jaiscr-2017-0001

Mots clésshort time series, forecasting, missing data, energy associated to series, complete and incomplete datasets

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
short time series, forecasting, missing data, energy associated to series, complete and incomplete datasets