Prediction of Sunspot Series Using a Recurrent Neural Network

Park, Dong-Chul

doi:10.1109/icisa.2011.5772339

2011 International Conference on Information Science and Applications 2011

DOI: 10.1109/icisa.2011.5772339

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Prediction of Sunspot Series Using a Recurrent Neural Network

Dong-Chul Park

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2016

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

Rivero

Pucheta

Laboret

et al. 2016

Journal of Artificial Intelligence and Soft Computing Research

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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.

show abstract

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

Rivero

Pucheta

Laboret

et al. 2016

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

show abstract

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Prediction of Sunspot Series Using a Recurrent Neural Network

Cited by 1 publication

References 17 publications

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

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

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