A Sequential Variational Mode Decomposition Method
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Abstract (arXiv)
In this paper, we introduce a sequential variational mode decomposition method to separate non-stationary mixed signals successively. This method is inspired by the variational method, and can precisely recover the original components one by one from the raw mixture without prior knowing or assuming the number of components. And in such a way, the mode number also can be determined during the separation procedure. Such character brings great convenience for real application and differs from the current VMD method. Furthermore, we also conduct a principal elongation for the mixture signal before the decomposing operation. By applying such an approach, the end effect can be reduced to a low level compared with the VMD method. To obtain higher accuracy, a refinement process has been introduced after gross extraction. Combined these techniques together, the final decomposition result implies a significant improvement compared with the VMD method and EMD method.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Mar 2021.
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Citing papers per year: 2024: 2.
2 papers.
- 2024A Combination Model Based on Sequential General Variational Mode Decomposition Method for Time Series Predictioncited by 1
- 2024A Combination Model for Time Series Prediction using LSTM via Extracting Dynamic Features Based on Spatial Smoothing and Sequential General Variational Mode Decompositioncited by 0
- 2024A Combination Model Based on Sequential General Variational Mode Decomposition Method for Time Series Prediction5 Jun 2024
- 2024A Combination Model for Time Series Prediction using LSTM via Extracting Dynamic Features Based on Spatial Smoothing and Sequential General Variational Mode Decomposition5 Jun 2024