Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python

Abstarct This work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in , $\textsf{R}$ $\textsf{MATLAB}$ . It aimed specifically for use by the insurance financial risk communities, applications such as return modelling, claims life with particular focus on mortality modelling. expands upon options methods available, improves their statistical robustness efficiency, providing robust, usable, reliable toolbox. Unlike many packages, allows customisation user, to ensure broader class of linear, non-linear, non-stationary time series analyses can be performed. The intrinsic functions (IMFs) extracted using contain complex multi-frequency structures which warrant maximum algorithmic effective analysis. A major contribution this intensive treatment edge effect most ubiquitous problem Various techniques, varying intricacy from numerous works, have been developed, refined, and, first time, compiled In addition effect, pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, other variations incorporated presented users paper supplementary materials provide several real-world actuarial user’s benefit.