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

Traditional time series methods struggle with non-stationary and non-linear real-world signals like financial curves or ECG traces. Empirical Mode Decomposition (EMD) offers a data-driven approach to decompose a complex signal into a set of clean oscillatory components called Intrinsic Mode Functions (IMFs). The algorithm iteratively identifies local extrema to create upper and lower envelopes, subtracts their mean to extract the fastest oscillation, and repeats this process on the remainder to isolate progressively slower components. While powerful and demonstrated with a Python example, EMD has limitations such as mode mixing and sensitivity to noise.