Variational Mode Decomposition

Variational Mode Decomposition (VMD) is a signal processing technique used to decompose complex time series data into simpler, meaningful components called intrinsic mode functions (IMFs), thereby mitigating the effects of noise and non-stationarity. Current research focuses on integrating VMD with various machine learning models, such as neural networks (e.g., LSTM, CNN) and other algorithms (e.g., ARIMA, GARCH), to improve forecasting accuracy in diverse applications including financial markets, energy systems, and traffic flow prediction. The effectiveness of VMD in enhancing the performance of these models across a range of datasets highlights its significance for improving time series analysis and forecasting across multiple scientific disciplines and practical applications.