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Avtomat. i Telemekh., 2014, Issue 5, Pages 143–158 (Mi at9099)  

This article is cited in 10 scientific papers (total in 10 papers)

Data Analysis

Forecasting nonstationary time series based on Hilbert–Huang transform and machine learning

V. G. Kurbatskya, D. N. Sidorovbac, V. A. Spiryaeva, N. V. Tomina

a Melentiev Energy Systems Institute, Siberian Branch, Russian Academy of Sciences, Irkutsk, Russia
b Irkutsk State University, Irkutsk, Russia
c National Research Irkutsk State Technical University, Irkutsk, Russia

Abstract: We propose a modification of the adaptive approach to time series forecasting. On the first stage, the original signal is decomposed with respect to a special empirical adaptive orthogonal basis, and the Hilbert's integral transform is applied. On the second stage, the resulting orthogonal functions and their instantaneous amplitudes are used as input variables for the machine learning unit that employs a hybrid genetic algorithm to train an artificial neural network and a regressive model based on support vector machines. The efficiency of the proposed approach is demonstrated on real data coming from Nord Pool Spot and Australian National Energy Market.

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English version:
Automation and Remote Control, 2014, 75:5, 922–934

Bibliographic databases:

Presented by the member of Editorial Board: Е. Я. Рубинович

Received: 04.04.2012

Citation: V. G. Kurbatsky, D. N. Sidorov, V. A. Spiryaev, N. V. Tomin, “Forecasting nonstationary time series based on Hilbert–Huang transform and machine learning”, Avtomat. i Telemekh., 2014, no. 5, 143–158; Autom. Remote Control, 75:5 (2014), 922–934

Citation in format AMSBIB
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\by V.~G.~Kurbatsky, D.~N.~Sidorov, V.~A.~Spiryaev, N.~V.~Tomin
\paper Forecasting nonstationary time series based on Hilbert--Huang transform and machine learning
\jour Avtomat. i Telemekh.
\yr 2014
\issue 5
\pages 143--158
\mathnet{http://mi.mathnet.ru/at9099}
\transl
\jour Autom. Remote Control
\yr 2014
\vol 75
\issue 5
\pages 922--934
\crossref{https://doi.org/10.1134/S0005117914050105}
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\scopus{https://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-84901256343}


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

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    2. V. Kornilov, V. Kurbatsky, N. Tomin, “Improving the principles of short-term electric load forecasting of the Irkutsk region”, Methodological problems in reliability study of large energy systems (RSES 2017), E3S Web of Conferences, 25, eds. N. Voropai, S. Senderov, A. Michalevich, H. Guliev, EDP Sciences, 2017, UNSP 03006  crossref  isi  scopus
    3. D. N. Karamov, I. V. Naumov, S. M. Perzhabinsky, “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
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