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Application of the Wavelet Data Transformation for the Time Series Forecasting by the Artificial Neural Network: book chapter. / Butorova, Anastasia; Baglaeva, Elena; Subbotina, Irina et al.
New Trends in the Applications of Differential Equations in Sciences: book. ed. / A. Slavova. Springer Cham, 2023. p. 365-370 (Springer Proceedings in Mathematics & Statistics; Vol. 412).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Butorova, A, Baglaeva, E, Subbotina, I, Sergeeva, M, Sergeev, A, Shichkin, A, Buevich, A & Petrov, P 2023, Application of the Wavelet Data Transformation for the Time Series Forecasting by the Artificial Neural Network: book chapter. in A Slavova (ed.), New Trends in the Applications of Differential Equations in Sciences: book. Springer Proceedings in Mathematics & Statistics, vol. 412, Springer Cham, pp. 365-370. https://doi.org/10.1007/978-3-031-21484-4_32

APA

Butorova, A., Baglaeva, E., Subbotina, I., Sergeeva, M., Sergeev, A., Shichkin, A., Buevich, A., & Petrov, P. (2023). Application of the Wavelet Data Transformation for the Time Series Forecasting by the Artificial Neural Network: book chapter. In A. Slavova (Ed.), New Trends in the Applications of Differential Equations in Sciences: book (pp. 365-370). (Springer Proceedings in Mathematics & Statistics; Vol. 412). Springer Cham. https://doi.org/10.1007/978-3-031-21484-4_32

Vancouver

Butorova A, Baglaeva E, Subbotina I, Sergeeva M, Sergeev A, Shichkin A et al. Application of the Wavelet Data Transformation for the Time Series Forecasting by the Artificial Neural Network: book chapter. In Slavova A, editor, New Trends in the Applications of Differential Equations in Sciences: book. Springer Cham. 2023. p. 365-370. (Springer Proceedings in Mathematics & Statistics). doi: 10.1007/978-3-031-21484-4_32

Author

Butorova, Anastasia ; Baglaeva, Elena ; Subbotina, Irina et al. / Application of the Wavelet Data Transformation for the Time Series Forecasting by the Artificial Neural Network : book chapter. New Trends in the Applications of Differential Equations in Sciences: book. editor / A. Slavova. Springer Cham, 2023. pp. 365-370 (Springer Proceedings in Mathematics & Statistics).

BibTeX

@inproceedings{cd3e7d5aecf7402f9f1622f7eef6d8a2,
title = "Application of the Wavelet Data Transformation for the Time Series Forecasting by the Artificial Neural Network: book chapter",
abstract = "The study tested how the wavelet transform of the data affects the accuracy of an artificial neural network model for forecasting surface methane concentration. A model based on the nonlinear autoregressive neural network with external input (NARX) was used. For comparison, we used the base NARX model and the hybrid model. The hybrid model was created based on the data to which the discrete wavelet transform (DWT) was applied. For DWT, the Daubechies wavelet of the fourth level was used. The initial data for the study were collected on the measurements of the concentration of greenhouse gases in the Russian Arctic zone. We evaluated the accuracy of the models by the following indicators: Mean absolute error, root mean square error, and the index of agreement. The proposed approach has improved the accuracy of the forecast. The accuracy of the hybrid model has increased by more than 10%.",
author = "Anastasia Butorova and Elena Baglaeva and Irina Subbotina and Marina Sergeeva and Aleksandr Sergeev and Andrey Shichkin and Alexander Buevich and Pavel Petrov",
note = "The equipment of the Common Use Center of Arctic Environmental Research of the Institute of Industrial Ecology of the Ural Branch of RAS was used to measure the concentration of greenhouse gases on Bely Island.",
year = "2023",
month = mar,
day = "18",
doi = "10.1007/978-3-031-21484-4_32",
language = "English",
isbn = "978-3-031-21483-7",
series = "Springer Proceedings in Mathematics & Statistics",
publisher = "Springer Cham",
pages = "365--370",
editor = "A. Slavova",
booktitle = "New Trends in the Applications of Differential Equations in Sciences",
address = "United Kingdom",

}

RIS

TY - GEN

T1 - Application of the Wavelet Data Transformation for the Time Series Forecasting by the Artificial Neural Network

T2 - book chapter

AU - Butorova, Anastasia

AU - Baglaeva, Elena

AU - Subbotina, Irina

AU - Sergeeva, Marina

AU - Sergeev, Aleksandr

AU - Shichkin, Andrey

AU - Buevich, Alexander

AU - Petrov, Pavel

N1 - The equipment of the Common Use Center of Arctic Environmental Research of the Institute of Industrial Ecology of the Ural Branch of RAS was used to measure the concentration of greenhouse gases on Bely Island.

PY - 2023/3/18

Y1 - 2023/3/18

N2 - The study tested how the wavelet transform of the data affects the accuracy of an artificial neural network model for forecasting surface methane concentration. A model based on the nonlinear autoregressive neural network with external input (NARX) was used. For comparison, we used the base NARX model and the hybrid model. The hybrid model was created based on the data to which the discrete wavelet transform (DWT) was applied. For DWT, the Daubechies wavelet of the fourth level was used. The initial data for the study were collected on the measurements of the concentration of greenhouse gases in the Russian Arctic zone. We evaluated the accuracy of the models by the following indicators: Mean absolute error, root mean square error, and the index of agreement. The proposed approach has improved the accuracy of the forecast. The accuracy of the hybrid model has increased by more than 10%.

AB - The study tested how the wavelet transform of the data affects the accuracy of an artificial neural network model for forecasting surface methane concentration. A model based on the nonlinear autoregressive neural network with external input (NARX) was used. For comparison, we used the base NARX model and the hybrid model. The hybrid model was created based on the data to which the discrete wavelet transform (DWT) was applied. For DWT, the Daubechies wavelet of the fourth level was used. The initial data for the study were collected on the measurements of the concentration of greenhouse gases in the Russian Arctic zone. We evaluated the accuracy of the models by the following indicators: Mean absolute error, root mean square error, and the index of agreement. The proposed approach has improved the accuracy of the forecast. The accuracy of the hybrid model has increased by more than 10%.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85151063841

U2 - 10.1007/978-3-031-21484-4_32

DO - 10.1007/978-3-031-21484-4_32

M3 - Conference contribution

SN - 978-3-031-21483-7

T3 - Springer Proceedings in Mathematics & Statistics

SP - 365

EP - 370

BT - New Trends in the Applications of Differential Equations in Sciences

A2 - Slavova, A.

PB - Springer Cham

ER -

ID: 37097000