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Empirical Mode Decomposition (EMD) is a data-driven signal processing technique that decomposes complex time series into clean oscillatory components called Intrinsic Mode Functions (IMFs). Unlike traditional methods that assume stationarity, EMD adaptively extracts patterns from non-stationary, non-linear signals by iteratively identifying local extrema and computing envelope means. The method works by sifting through the signal to isolate oscillations from fastest to slowest, plus a residual trend. While powerful for analyzing real-world signals like ECG traces, financial data, and sensor readings, EMD has limitations including mode mixing, noise sensitivity, and lack of mathematical guarantees. This is the first part of a series covering adaptive decomposition methods, with VMD and MVMD to follow.

Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series