Welcome to HiNTS’s documentation!

HiNTS (Higher-Order Interactions in N-Dimensional Time Series)

This repository hosts the source code and tutorial notebooks for HiNTS, a Python package dedicated to the sophisticated analysis of complex systems via multidimensional time series data Akhshi et al. (2024). It offers a set of functions and tools for detecting and quantifying the directions and strengths of interactions in both deterministic and stochastic interactions within complex systems, encompassing pairwise to higher-order interactions (Akhshi et al. (2024) & Tabar et al. (2024)). The main function uses a data-driven approach for characterizing interactions of different orders based on solving a set of linear equations constructed from Kramers-Moyal coefficients derived from statistical moments of N-dimensional multivariate time series. It makes use of the method described by (Akhshi et al. (2024), Tabar et al. (2024), and Nikakhtar et al. (2023)).

Features

  • Universality and applications: This package is designed to be user-friendly and accessible to a wide range of users, including researchers and practitioners from various fields such as physics, biology, economics, climatology, and engineering, etc.

  • Multidimensional Time Series Processing: Capable of handling and analyzing data measured from subsystems of a complex system.

  • Higher-Order Interaction Detection: Identifies the directions and strengths of interactions, encompassing both pairwise and higher-order interactions within a complex system.

  • Identification and Quantification of Directions and Strength of Interactions: Quantifies both the strengths and directions of interactions in various orders within both the deterministic and stochastic components of a complex system dynamics.

  • Robust Mathematical Foundation: Based on a solid theoretical framework involving estimations of Kramers-Moyal coefficients from N-dimensional time series.

Installation

To install HiNTS, you can either use pip or install it directly from the source:

pip install hints-kmcs

If you prefer installing from the source, clone the repository and install using the setup script:

git clone https://github.com/aminakhshi/hints.git
cd hints
pip install .

HiNTS runs on NumPy and needs no GPU. If you work with long multivariate records and have one available, you can optionally install the PyTorch backend and pass backend='torch', device='cuda' to move the heavy moment accumulation onto the GPU:

pip install hints-kmcs[torch]

This is entirely optional: the standard installation above never requires PyTorch or a GPU, and the results are the same either way. To run the example notebooks, install the extras they need with pip install hints-kmcs[examples].

Usage

Below is a basic example of using HiNTS. For more detailed examples and tutorials, please refer to our documentation at notebooks and the corresponding papers Akhshi et al. (2024) and Tabar et al. (2024).

import hints

# Setting initial parameters for the interaction coefficient calculator
dt = 1  # Time interval between data points, (1/sampling rate)
orders = [0, 1]  # Defines interaction orders: 0 for constant coefficients (alpha), 1 for pairwise interactions, etc.
mode = 'drift'  # Specifies the type of coefficients to be estimated (choices: 'drift', 'diffusion')

# Importing time series data as a NumPy array or a pandas DataFrame
# You need to 'example.csv' with the path to your data file. The function will automatically detect the format.
# You can also pass the data as a NumPy array.

calulator = hints.kmcc('example.csv', dt=dt, interaction_order=[0,1,2], estimation_mode='drift')


# Computing interaction coefficients
interaction_coefficients = calulator.get_coefficients()

# Display the results
print(interaction_coefficients)

In diffusion mode, get_coefficients() returns the expansion of D^(2)(x). The noise amplitude G(x) of the Langevin equation, defined by G G^T = D^(2), can be obtained directly from the same calculator:

diffusion = hints.kmcc('example.csv', dt=dt, interaction_order=[0], estimation_mode='diffusion')

D = diffusion.get_diffusion_matrix()   # (n_points, N, N)
G = diffusion.get_noise_amplitude()    # (n_points, N, N), lower triangular by default

Documentation

For more detailed usage and API documentation, please refer to our documentation.

Contributing

We welcome contributions from the community. If you wish to contribute, please check out our contribution guidelines.

Authors

Version History

For a detailed list of changes for each version of the project, see the Changelog. Currently, the project is in the beta stage and there might be some bugs and issues. We are working on improving the package and adding more features.

  • 0.1.3

    • Reconstruction of the noise amplitude matrix G(x) from the diffusion coefficients

    • Fixed loading time series from a file, which previously raised a TypeError

    • Consistent coefficient labels (F_x1..., D_x1x1...) and clearer input validation

    • Reports the condition number of the moment matrix and offers least-squares solvers

    • Optional PyTorch backend for large datasets, and a regression test suite

  • 0.1.x(2024-04-01)

    • Initial beta release

Citation

If you use HiNTS in your research, please cite our work as follows:

@article{hints2024,
  title={HiNTS: Higher-Order Interactions in N-Dimensional Time Series},
  author={Akhshi et al.},
  journal={#},
  year={2024},
  publisher={#}
}

@article{revealing2024,
  title = {Revealing Higher-Order Interactions in High-Dimensional Complex Systems: A Data-Driven Approach},
  author = {Tabar, M. Reza Rahimi and Nikakhtar, Farnik and Parkavousi, Laya and Akhshi, Amin and Feudel, Ulrike and Lehnertz, Klaus},
  journal = {Phys. Rev. X},
  volume = {14},
  issue = {1},
  pages = {011050},
  numpages = {36},
  year = {2024},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevX.14.011050},
  url = {https://link.aps.org/doi/10.1103/PhysRevX.14.011050}
}

@article{reconstruction2023,
  title = {Data-driven reconstruction of stochastic dynamical equations based on statistical moments},
  author = {Nikakhtar, Farnik and Parkavousi, Laya and Sahimi, Muhammad and Tabar, M Reza Rahimi and Feudel, Ulrike and Lehnertz, Klaus},
  journal = {New Journal of Physics},
  volume = {25},
  number = {8},
  pages = {083025},
  year = {2023},
  publisher = {IOP Publishing},
  doi = {10.1088/1367-2630/acec63},
}

Contact

For any questions or feedback, please reach out to us at amin.akhshi@gmail.com.

License

HiNTS is licensed under the MIT License. See the LICENSE file for more details.

Contents:

Indices and tables