Lasso Regression Based on Empirical Mode Decomposition

Lei Qin, Shuangge Ma, Jung Chen Lin, Ben Chang Shia

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


The Hilbert-Huang transform uses the empirical mode decomposition (EMD) method to analyze nonlinear and nonstationary data. This method breaks a time series of data into several orthogonal sequences based on differences in frequency. These data components include the intrinsic mode functions (IMFs) and the final residue. Although IMFs have been used in the past as predictors for other variables, very little effort has been devoted to identifying the most effective predictors among IMFs. As lasso is a widely used method for feature selection within complex datasets, the main objective of this article is to present a lasso regression based on the EMD method for choosing decomposed components that exhibit the strongest effects. Both numerical experiments and empirical results show that the proposed modeling process can use time-frequency structure within data to reveal interactions between two variables. This allows for more accurate predictions concerning future events.

Original languageEnglish
Pages (from-to)1281-1294
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Issue number4
Publication statusPublished - Apr 20 2016
Externally publishedYes


  • EMD
  • Lasso
  • Time-frequency structure relationship
  • Two-variable model

ASJC Scopus subject areas

  • Modelling and Simulation
  • Statistics and Probability


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