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Time-dependent Prediction Degredation Assessment of Neural-networks-based Tec Forecasting Models : Volume 10, Issue 6 (30/11/-0001)

By Xenos, Th. D.

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Book Id: WPLBN0004019577
Format Type: PDF Article :
File Size: Pages 3
Reproduction Date: 2015

Title: Time-dependent Prediction Degredation Assessment of Neural-networks-based Tec Forecasting Models : Volume 10, Issue 6 (30/11/-0001)  
Author: Xenos, Th. D.
Volume: Vol. 10, Issue 6
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
-0001
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Kouris, S. S., Casimiro, A., & Xenos, T. D. (-0001). Time-dependent Prediction Degredation Assessment of Neural-networks-based Tec Forecasting Models : Volume 10, Issue 6 (30/11/-0001). Retrieved from http://worldlibrary.in/


Description
Description: Department of Electrical and Comp. Eng., Univ. of Thessaloniki, Greece. An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of ± 10% is considered, the population percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the foF2 - TEC variability difference and on hysteresis-like effect between these two ionospheric characteristics.

Summary
Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models

 

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