PyEMD

PyEMD implements Empirical Mode Decomposition (EMD) in Python to decompose non-linear, non-stationary oscillatory time series into intrinsic mode functions for analysis of temporal dynamics.

Key Features:

• Empirical Mode Decomposition (EMD) implementation: A Python implementation of the EMD method for decomposing signals into intrinsic mode functions (IMFs).
• Sifting Algorithms: Implements a variety of sifting algorithms to extract IMFs from non-linear and non-stationary data.
• Instantaneous Frequency Transformations: Provides transformations to compute instantaneous frequency evolution over time.
• Power Spectrum Construction: Supports construction of power spectra to analyze signal energy distribution across frequency components.
• Single-Cycle Feature Analysis: Facilitates analysis of individual oscillatory cycles to identify transient phenomena and cycle-specific features.

Scientific Applications:

• Biomedical signal processing: Decomposition and frequency analysis of physiological time series such as EEG or ECG.
• Geophysical data analysis: Analysis of non-linear, non-stationary signals in geophysics such as seismic or climate time series.
• Financial market prediction: Decomposition and temporal-frequency analysis of financial time series to investigate market dynamics.
• Cross-domain temporal dynamics analysis: Examination of non-linear and non-stationary oscillatory time series across diverse scientific domains.

Methodology:

Implements the Empirical Mode Decomposition (EMD) method using sifting algorithms to extract intrinsic mode functions, and provides instantaneous frequency transformations, power spectrum construction, and single-cycle feature analysis; EMD is an adaptive, data-driven approach that does not assume linearity or stationarity.