ADAnalysis for Matlab™ is a software library that allows an adaptive data analysis approache for the various applications in a number of scientific fields.



Key features of the library:

  • decomposition of the time-series into empirical modes (IMF) with EMD, EEMD, CEEMDAN (optimized for multi-core processors and computer clusters)
  • time-series noise components filtering (i.e. smoothing)
  • time-series trend-cyclic component estimation and stochastic part filtering
  • Hilbert transform and calculation of the smoothed instantaneous characteristics of the analytical signal
  • calculation of time-dependent intrinsic correlation (TDIC) and local sliding-window correlation for empirical modes
  • estimation of time-dependent intrinsic regression (TDIR) in adaptive sliding-window for empirical modes.

Library source code is available under the GNU GPL v.2 license in ADAnalysis repository on GitHub.


  • Afanasyev, D., Fedorova, E., Popov, V., 2015. Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis. Energy Economics 51, 215-226.
  • Afanasyev, D., Fedorova, E., 2016. The long-term trends on the electricity markets: comparison of empirical mode and wavelet decompositions. Energy Economics 56, 432-442.
  • Chen, N., Wu, Z., Huang, N., 2010. The time-dependent intrinsic correlation based on the empirical mode decomposition. Advances in Adaptive Data Analysis 2 (2), 223-265.
  • Colominas, M., Schlotthauer, G., Torres, M., Flandrin, P., 2012. Noise-assisted EMD methods in action. Advances in Adaptive Data Analysis 4 (4).
  • Flandrin, P., Goncalves, P., Rilling, G., 2004. Detrending and denoising with empirical mode decomposition. EU- SIPCO.
  • Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C., Liu, H., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Royal Soc. London A 454 (1971), 903-995.
  • Moghtader, A., Borgnat, P., Flandrin, P., 2011. Trend filtering: empirical mode decomposition versus l1 and Hodrick-Prescott. Advances in Adaptive Data Analysis 3, 41-61.
  • Papadimitriou, S., Sun, J., Yu, P., 2006. Local correlation tracking in time series. ICDM, 456-465.
  • Torres, M., Colominas M., Schlotthauer, G., Flandrin, P., 2011. A complete Ensemble Empirical Mode decomposition with adaptive noise. IEEE Int. Conf. on Acoust., Speech and Signal Proc. ICASSP-11, pp. 4144-4147, Prague (CZ)
  • Wu, Z., Huang, N., 2009. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis 1 (1), 1–41.
  • EMD and EEMD impementation for Matlab
  • CEEMDAN impementation for Matlab