Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103215
Title: Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model
Authors: Sergeev, A.
Shichkin, A.
Buevich, A.
Baglaeva, E.
Subbotina, I.
Rakhmatova, A.
Kosachenko, A.
Moskaleva, A.
Medvedev, A.
Sergeeva, M.
Issue Date: 2020
Publisher: American Institute of Physics Inc.
Citation: Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model / A. Sergeev, A. Shichkin, A. Buevich, et al. — DOI 10.1063/5.0027179 // AIP Conference Proceedings. — 2020. — Vol. 2293. — 120021.
Abstract: An algorithm based on predicting the residuals of the nonlinear autoregressive neural network model with external input (NARX), which can improve the prediction accuracy, was proposed. Data of the concentration of one of the main greenhouse gases methane (CH4) on the Arctic Island of Belyy, Russia, were used for prediction. A time interval, which was characterized by high daily fluctuations in the CH4 concentration was selected. The forecast accuracy was determined by the mean absolute error (MAE), root mean squared error (RMSE) and root mean squared relative error (RMSRE) errors. The use of the algorithm allowed to increase the forecast accuracy from 11% for RMSE to 20% for RMSRE. © 2020 American Institute of Physics Inc.. All rights reserved.
URI: http://hdl.handle.net/10995/103215
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85098008756
PURE ID: 20386777
1776d91d-8916-4a9a-a442-36e16c687ed9
ISSN: 0094243X
ISBN: 9780735440258
DOI: 10.1063/5.0027179
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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