https://github.com/keichi/kedm

A high-performance implementation of Empirical Dynamic Modeling (EDM)
https://github.com/keichi/kedm

empirical-dynamic-modeling high-performance-computing nonlinear-dynamics time-series

Last synced: 10 months ago
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A high-performance implementation of Empirical Dynamic Modeling (EDM)

• Host: GitHub
• URL: https://github.com/keichi/kedm
• Owner: keichi
• License: mit
• Created: 2020-05-02T13:48:10.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-09-03T22:17:47.000Z (almost 2 years ago)
• Last Synced: 2024-12-10T17:17:22.544Z (over 1 year ago)
• Topics: empirical-dynamic-modeling, high-performance-computing, nonlinear-dynamics, time-series
• Language: Python
• Homepage: https://kedm.readthedocs.io/
• Size: 553 KB
• Stars: 15
• Watchers: 4
• Forks: 5
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# kEDM

[![build](https://github.com/keichi/kEDM/workflows/build/badge.svg)](https://github.com/keichi/kEDM/actions?query=workflow%3Abuild) [![Documentation Status](https://readthedocs.org/projects/kedm/badge/?version=latest)](https://kedm.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/kedm.svg)](https://badge.fury.io/py/kedm)

kEDM (Kokkos-EDM) is a high-performance implementation of the [Empirical

Dynamical Modeling (EDM)](https://sugiharalab.github.io/EDM_Documentation/)

framework. The goal of kEDM is to provide an optimized and parallelized

implementation of EDM algorithms for high-end CPUs and GPUs, while ensuring

compatibility with the original reference implementation

([cppEDM](https://github.com/SugiharaLab/cppEDM)).

Following EDM algorithms are currently implemented in kEDM:

- Simplex projection [1]

- Sequential Locally Weighted Global Linear Maps (S-Map) [2]

- Convergent Cross Mapping (CCM) [3]

## Installation

CPU (Linux and macOS)

```

pip3 install kedm

```

NVIDIA GPU (CUDA 11.2 or later)

```

pip3 install kedm-11x

```

NVIDIA GPU (CUDA 12.0 or later)

```

pip3 install kedm-12x

```

## Citing

Please cite the following papers if you find kEDM useful:

- Keichi Takahashi, Kohei Ichikawa, Joseph Park, Gerald M. Pao, “Scalable Empirical Dynamic Modeling

  with Parallel Computing and Approximate k-NN Search,” IEEE Access, vol. 11, pp. 68171–68183,

  Jun. 2023. [10.1109/ACCESS.2023.3289836](https://doi.org/10.1109/ACCESS.2023.3289836)

- Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park,

  Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao, "kEDM: A

  Performance-portable Implementation of Empirical Dynamical Modeling," Practice

  & Experience in Advanced Research Computing (PEARC 2021), Jul. 2021.

  [10.1145/3437359.3465571](https://doi.org/10.1145/3437359.3465571)

## References

1. George Sugihara, Robert May, "Nonlinear forecasting as a way of

   distinguishing chaos from measurement error in time series," Nature, vol.

   344, pp. 734–741,  1990. [10.1038/344734a0](https://doi.org/10.1038/344734a0)

2. George Sugihara, "Nonlinear forecasting for the classification of natural

   time series. Philosophical Transactions," Physical Sciences and Engineering,

   vol. 348, no. 1688, pp. 477–495, 1994.

   [10.1098/rsta.1994.0106](https://doi.org/10.1098/rsta.1994.0106)

3. George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael

   Fogarty, Stephan Munch, "Detecting Causality in Complex Ecosystems,"

   Science, vol. 338, pp. 496–500, 2012.

   [10.1126/science.1227079](https://doi.org/10.1126/science.1227079)