rEDM: Empirical dynamic modeling

In rEDM: Empirical Dynamic Modeling ('EDM')

Empirical dynamic modeling

Description

rEDM provides tools for Empirical Dynamic Modeling time series analyses. It is based on reconstructing multivariate state space representations from uni- or multivariate time series, then projecting state changes using various metrics applied to nearest neighbors.

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)

Details

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

Author(s)

Maintainer: Joseph Park

Authors: Joseph Park, Ethan Deyle, George Sugihara

References

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. 30, 2026, 9:07 a.m.