Power system parameters forecasting using Hilbert-Huang transform and machine learning

zbMath1343.62083arXiv1404.2353MaRDI QIDQ278553

Victor Grigorevich Kurbatsky, Vadim Aleksandrovich Spiryaev, Paul Leahy, Denis Nikolaevich Sidorov, Nikita Viktorovich Tomin, Alekseĭ Vital'evich Zhukov

Publication date: 2 May 2016

Published in: Izvestiya Irkutskogo Gosudarstvennogo Universiteta. Seriya Matematika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.2353

zbMATH Keywords

singular integralintegral transformstime seriesforecastingmachine learningboostingANNfeature analysisSVM

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• L-BFGS-B

Cites Work

• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Forecasting nonstationary time series based on Hilbert-Huang transform and machine learning
• Solvability of systems of Volterra integral equations of the first kind with piecewise continuous kernels
• Algorithm 778: L-BFGS-B
• Random forests
• Stochastic gradient boosting.

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