Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability

Dimitrios Giannakis, Andrew J. Majda

Research output: Contribution to journalArticlepeer-review


Many processes in science and engineering develop multiscale temporal and spatial patterns, with complex underlying dynamics and time-dependent external forcings. Because of the importance in understanding and predicting these phenomena, extracting the salient modes of variability empirically from incomplete observations is a problem of wide contemporary interest. Here, we present a technique for analyzing high-dimensional, complex time series that exploits the geometrical relationships between the observed data points to recover features characteristic of strongly nonlinear dynamics (such as intermittency and rare events), which are not accessible to classical singular spectrum analysis. The method employs Laplacian eigenmaps, evaluated after suitable time-lagged embedding, to produce a reduced representation of the observed samples, where standard tools of matrix algebra can be used to perform truncated singular-value decomposition despite the nonlinear geometrical structure of the dataset. We illustrate the utility of the technique in capturing intermittent modes associated with the Kuroshio current in the North Pacific sector of a general circulation model and dimensional reduction of a low-order atmospheric model featuring chaotic intermittent regime transitions, where classical singular spectrum analysis is already known to fail dramatically.

Original languageEnglish (US)
Pages (from-to)2222-2227
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number7
StatePublished - Feb 14 2012


  • Decadal climate variability
  • Manifold embedding
  • Regime behavior
  • Spatio-temporal analysis

ASJC Scopus subject areas

  • General


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