Multi Scale Decomposition

Multi-scale decomposition techniques analyze data across different scales of resolution, aiming to extract meaningful information from complex systems by separating components at various levels of detail. Current research focuses on applying these methods across diverse fields, employing machine learning models like neural networks (including transformers and MLPs) and wavelet transforms, alongside more traditional signal processing approaches. This work is significant for improving the accuracy and efficiency of tasks ranging from time series forecasting and image processing to path planning and the analysis of complex signals like sea surface height and electromyograms, ultimately leading to more robust and insightful models in various scientific and engineering domains.