This tutorial explores the class of non-parametric time series basis decomposition methods particularly suited for non-stationary time series known as Empirical Mode Decomposition (EMD). A detailed review of the state of the art statistical approaches that combine finite basis signal...

This paper introduces a new Empirical Mode Decomposition (EMD) Python package called AdvEMDpy that is demonstrably more flexible and which generalises in numerous important ways the existing EMD packages available in Python, R, and MATLAB. The extensions introduced by this AdvEMDpy package both...

This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py,...

A time-series basis decomposition and trend extraction technique known as Empirical Mode Decomposition (EMD), designed for multi-scale time-frequency decomposition in non-stationary time-series settings, will be combined with Regularised Covariance Regression (RCR) methods to produce a framework...

This work serves as a formal supplement to ‘Package AdvEMDpy: Algorithmic Variations of Empirical Mode Decomposition in Python’ with additional synthetic and real-world examples. AdvEMDpy will be shown to be more accurate than its Python competitors in resolving the underlying driving...