- Tytuł:
- Energy associated tuning method for short-term series forecasting by complete and incomplete datasets
- Autorzy:
-
Rodríguez-Rivero, C.
Pucheta, J.
Laboret, S.
Sauchelli, V.
Patińo, D. - Powiązania:
- https://bibliotekanauki.pl/articles/91842.pdf
- Data publikacji:
- 2017
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
short time series
forecasting
missing data
energy associated to series
complete datasets
incomplete datasets - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 1; 5-16
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł