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

DOI10.1134/S0005117914050105zbMath1306.62199OpenAlexW1963533896MaRDI QIDQ463396

Denis Nikolaevich Sidorov, Vadim Aleksandrovich Spiryaev, Nikita Viktorovich Tomin, Victor Grigorevich Kurbatsky

Publication date: 16 October 2014

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0005117914050105

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

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

Uses Software

• L-BFGS-B
• itsmr

Cites Work

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• Thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm
• Multilayer feedforward networks are universal approximators
• Nonlinear time series. Nonparametric and parametric methods
• Algorithm 778: L-BFGS-B
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Introduction to Time Series and Forecasting
• Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition
• Random forests
• Stochastic gradient boosting.

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