Variational Mode Decomposition

By Konstantin Dragomiretskiy, Dominique Zosso. IEEE Transactions on Signal Processing, 2014

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Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting (2019, methodology): “Another novel method for time-series forecasting is a hybrid multiscale approach, such as empirical mode decomposition (EMD) [25], ensemble EMD (EEMD) [26], multi-level wavelet decomposition network (mWDN) [27] and variational mode decomposition (VMD) [28].”
Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model (2020, methodology): “The VMD theory abandons the signal stripping method used in EMD theory when obtains IMF components.”
Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery (2018, methodology): “…PN n= 1 x nð Þ tp1ð Þ4 N tp12 tp7 tp2 Where x nð Þ is a sampling series of raw signal with N points. for vibration signal of rotating machinery, variational mode decomposition (VMD) (Dragomiretskiy and Zosso, 2014) is utilized to decompose the vibration signal into several stable components,…”
Stock closing price prediction based on sentiment analysis and LSTM (2019, methodology): “EMD is a common method to process time series and has been applied in various ﬁelds [14].”
Successive variational mode decomposition (2020, positive): “Unlike EMD, Variational Mode Decomposition (VMD) method is fully established on a mathematical framework [15] .”