Weighted sliding Empirical Mode Decomposition

نویسندگان

  • Rupert Faltermeier
  • Angela Zeiler
  • Ana Maria Tomé
  • Alexander Brawanski
  • Elmar Wolfgang Lang
چکیده

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis

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عنوان ژورنال:
  • Advances in Adaptive Data Analysis

دوره 3  شماره 

صفحات  -

تاریخ انتشار 2011