Avtomatika i Telemekhanika
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Impact factor
Guidelines for authors
Submit a manuscript

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Avtomat. i Telemekh.:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Avtomat. i Telemekh., 2011, Issue 7, Pages 58–68 (Mi at2244)  

This article is cited in 9 scientific papers (total in 9 papers)

System Analysis and Operations Research

On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert–Huang transform

V. G. Kurbatskii, D. N. Sidorov, V. A. Spiryaev, N. V. Tomin

Melentiev Energy Systems Institute, Siberian Branch, Russian Academy of Sciences, Irkutsk, Russia

Abstract: The two-stage adaptive approach for time series forecasting is proposed. The first stage involves the decomposition of the initial time series into basis functions and application to them of the Hilbert transform. At the second stage the obtained functions and their instantaneous amplitudes are used as input variables of neural network forecasting. The efficiency of the developed approach is displayed in real time series in the electric power problem of forecasting the sharply variable implementations of active power flows.

Full text: PDF file (975 kB)
References: PDF file   HTML file

English version:
Automation and Remote Control, 2011, 72:7, 1405–1414

Bibliographic databases:

Presented by the member of Editorial Board: . . 

Received: 16.12.2010

Citation: V. G. Kurbatskii, D. N. Sidorov, V. A. Spiryaev, N. V. Tomin, “On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert–Huang transform”, Avtomat. i Telemekh., 2011, no. 7, 58–68; Autom. Remote Control, 72:7 (2011), 1405–1414

Citation in format AMSBIB
\Bibitem{KurSidSpi11}
\by V.~G.~Kurbatskii, D.~N.~Sidorov, V.~A.~Spiryaev, N.~V.~Tomin
\paper On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert--Huang transform
\jour Avtomat. i Telemekh.
\yr 2011
\issue 7
\pages 58--68
\mathnet{http://mi.mathnet.ru/at2244}
\mathscinet{http://www.ams.org/mathscinet-getitem?mr=2867000}
\transl
\jour Autom. Remote Control
\yr 2011
\vol 72
\issue 7
\pages 1405--1414
\crossref{https://doi.org/10.1134/S0005117911070083}
\isi{http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&DestLinkType=FullRecord&DestApp=ALL_WOS&KeyUT=000297403900008}
\scopus{https://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-80052564385}


Linking options:
  • http://mi.mathnet.ru/eng/at2244
  • http://mi.mathnet.ru/eng/at/y2011/i7/p58

    SHARE: VKontakte.ru FaceBook Twitter Mail.ru Livejournal Memori.ru


    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. Kurbatsky V.G., Tornin N.V., “Smart System of Monitoring and Forecasting for Electric Power Systems”, 2012 IEEE International Conference on Power System Technology (Powercon), IEEE, 2012  isi
    2. Voropai N.I., Efimov D.N., Kolosok I.N., Kurbatsky V.G., Glazunova A.M., Korkina E.S., Osak A.B., Tomin N.V., Panasetsky D.A., “Smart Technologies in Emergency Control of Russia's Unified Energy System”, IEEE Trans. Smart Grid, 4:3 (2013), 1732–1740  crossref  isi  elib  scopus
    3. V. G. Kurbatsky, V. A. Spiryaev, N. V. Tomin, P. Leahy, D. N. Sidorov, A. V. Zhukov, “Power System Parameters Forecasting Using Hilbert–Huang Transform and Machine Learning”, Izvestiya Irkutskogo gosudarstvennogo universiteta. Seriya Matematika, 9 (2014), 75–90  mathnet
    4. Masselot P., Chebana F., Belanger D., St-Hilaire A., Abdous B., Gosselin P., Ouarda Taha B. M. J., “Emd-Regression For Modelling Multi-Scale Relationships, and Application to Weather-Related Cardiovascular Mortality”, Sci. Total Environ., 612 (2017), 1018–1029  crossref  isi  scopus
    5. Eseye A.T., Zhang J., Zheng D., “Short-Term Photovoltaic Solar Power Forecasting Using a Hybrid Wavelet-Pso-Svm Model Based on Scada and Meteorological Information”, Renew. Energy, 118 (2018), 357–367  crossref  isi  scopus
    6. Karamov D.N., Naumov I.V., Perzhabinsky S.M., “Mathematical Modelling of Failures of Electrical Grid (10 Kv) of Autonomous Energy Systems With Renewable Distributed Generation”, Bull. Tomsk Polytech. Univ.-Geo Assets Eng., 329:7 (2018), 116–130  isi
    7. Xu J., Wang Zh., Tan Ch., Lu D., Wu B., Su Zh., Tang Ya., “Cutting Pattern Identification For Coal Mining Shearer Through Sound Signals Based on a Convolutional Neural Network”, Symmetry-Basel, 10:12 (2018), 736  crossref  isi  scopus
    8. Voropai N., Efimov D., Kolosok I., Kurbatsky V., Glazunova A., Korkina E., Tomin N., Panasetsky D., “Intelligent Control and Protection in the Russian Electric Power System”, Application of Smart Grid Technologies: Case Studies in Saving Electricity in Different Parts of the World, eds. Lamont L., Sayigh A., Academic Press Ltd-Elsevier Science Ltd, 2018, 61–140  crossref  isi
    9. Qu Z., Mao W., Zhang K., Zhang W., Li Zh., “Multi-Step Wind Speed Forecasting Based on a Hybrid Decomposition Technique and An Improved Back-Propagation Neural Network”, Renew. Energy, 133 (2019), 919–929  crossref  isi  scopus
  • Avtomatika i Telemekhanika
    Number of views:
    This page:466
    Full text:131
    References:36
    First page:30

     
    Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2021