EDM: Empirical dynamic modeling

EDMR Documentation

Empirical dynamic modeling


EDM provides tools for data-driven time series analyses. It is based on reconstructing multivariate state (or phase) space representations from uni or multivariate time series, then projecting state changes using various metrics applied to nearest neighbors.

EDM is a Rcpp interface to the cppEDM library of Empirical Dynamic Modeling tools. Functionality includes:

  • Simplex projection (Sugihara and May 1990)

  • Sequential Locally Weighted Global Linear Maps (S-map) (Sugihara 1994)

  • Multivariate embeddings (Dixon et. al. 1999)

  • Convergent cross mapping (Sugihara et. al. 2012)

  • Multiview embedding (Ye and Sugihara 2016)


Main Functions:

  • Simplex - simplex projection

  • SMap - S-map projection

  • CCM - convergent cross mapping

  • Multiview - multiview forecasting

Helper Functions:

  • Embed - time delay embedding

  • ComputeError - forecast skill metrics

  • EmbedDimension - optimal embedding dimension

  • PredictInterval - optimal prediction interval

  • PredictNonlinear - evaluate nonlinearity


Maintainer: Joseph Park & Cameron Smith

Authors: Joseph Park, Cameron Smith, Ethan Deyle, Erik Saberski, George Sugihara


Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734-741.

Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688) : 477-495.

Dixon, P. A., M. Milicich, and G. Sugihara, 1999. Episodic fluctuations in larval supply. Science 283:1528-1530.

Sugihara G., May R., Ye H., Hsieh C., Deyle E., Fogarty M., Munch S., 2012. Detecting Causality in Complex Ecosystems. Science 338:496-500.

Ye H., and G. Sugihara, 2016. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science 353:922-925.

rEDM documentation built on Aug. 6, 2022, 5:08 p.m.