Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python

Cole van Jaarsveldt, Matthew Ames, Gareth W. Peters, Mike Chantler

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
62 Downloads (Pure)


This work presents a EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in Python, R, and MATLAB. It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.
Original languageEnglish
Pages (from-to)606-642
Number of pages37
JournalAnnals of Actuarial Science
Issue number3
Early online date5 May 2023
Publication statusPublished - Nov 2023


  • Downsampling
  • Empirical Mode Decomposition (EMD)
  • Enhanced EMD (EEMD)
  • Ensemble EMD
  • Filtering
  • Graduation
  • Hilbert transform
  • Knot optimisation
  • Python
  • R
  • Splines
  • Statistical EMD (SEMD)
  • Time series analysis
  • Winsorization

ASJC Scopus subject areas

  • Economics and Econometrics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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