Module emd

iron_wave::transform

Module emd 

Source
Expand description

Empirical Mode Decomposition (EMD) for non-stationary signal analysis

EMD is a data-driven method for decomposing signals into Intrinsic Mode Functions (IMFs). Unlike wavelet transforms, EMD doesn’t require predefined basis functions and adapts to the local characteristics of the signal.

§Financial Applications

  • Trend extraction: Separate long-term trends from short-term fluctuations
  • Volatility decomposition: Multi-scale volatility analysis
  • Cycle detection: Identify market cycles without assuming periodicity
  • Noise removal: Extract market microstructure noise
  • Regime identification: Detect changes in market dynamics

§EEMD Random Seed Configuration

The Ensemble EMD (EEMD) implementation supports configurable random seeds:

  • Production use: Set random_seed: None for true randomness (default)
  • Testing/Research: Use with_seed(seed) or set random_seed: Some(seed) for reproducibility
use iron_wave::transform::EEMD;

// For production - uses random seed
let eemd = EEMD::default();

// For testing/research - reproducible results
let eemd_reproducible = EEMD::default().with_seed(42);

Modules§

financial
Financial-specific EMD analysis

Structs§

EEMD
Ensemble Empirical Mode Decomposition (EEMD) for improved robustness
EMDConfig
Configuration for EMD decomposition
EMDResult
EMD result containing all IMFs and the residue
IMF
Numerical tolerance for singularity detection in cubic spline interpolation

Functions§

emd
Perform Empirical Mode Decomposition