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Avtomatika i Telemekhanika, 2011, Issue 7, Pages 58–68
(Mi at2244)
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This article is cited in 27 scientific papers (total in 27 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.
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
Linking options:
https://www.mathnet.ru/eng/at2244 https://www.mathnet.ru/eng/at/y2011/i7/p58
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Statistics & downloads: |
Abstract page: | 600 | Full-text PDF : | 182 | References: | 68 | First page: | 30 |
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