generate-corr-sequence

The simulation of stationary time-series (discrete-time random process) with a specific autocorrelation function (ACF) and continuous probability distribution.

https://github.com/bykhov/generate_corr_sequence

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Keywords

autocorrelation autocovariance python random-process simulation time-series wss

Last synced: 6 months ago · JSON representation ·

Repository

The simulation of stationary time-series (discrete-time random process) with a specific autocorrelation function (ACF) and continuous probability distribution.

Basic Info

Host: GitHub
Owner: bykhov
License: other
Language: Python
Default Branch: main
Homepage:
Size: 14.1 MB

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Topics

autocorrelation autocovariance python random-process simulation time-series wss

Created almost 3 years ago · Last pushed about 2 years ago

Metadata Files

Readme License Citation

Generate stationary correlated time-series

Summary

This Python function creates a time-series (discrete-time random process) with a specific autocorrelation function (ACF) and continuous probability distribution, e.g with predefined probability density function (PDF).

Usage:

python from generate_corr_sequence import gen_corr_sequence gen_corr_sequence(dist_obj=uniform, target_acf=1 - np.minimum(np.arange(0, 100), 100) / 100, L: int = 2 ** 20, seed=None, debug: bool = False, plot_figures_name: str = None)

Arguments

dist_obj - A continuous distribution object from scipy.stats, default is uniform
target_acf - A required ACF function, default is a linear function
L - Number of required samples
seed - Seed of the random number generator
debug - Plots PDF and ACF graphs
plot_figures_name - Filename of the debug figure to be saved, only when debug is True. In no extension is provided, the default is png.

Returns:

A random sequence with pre-defined ACF and distribution with type numpy.ndarray.

Examples

Default settings with uniform distribution and linear ACF

The example below shows the default settings of the function from the examples/default_settings_example.ipynb file.

```python

Example usage of the function with default settings

sequence = gencorrsequence(debug=True) ```

Debug figure

Nakagami distribution with Bessel function ACF

The example below is from the examples/nakagami_example.ipynb file.

```python

Example usage of the function with Nakagami distribution and an autocorrelation function

from generatecorrsequence import gencorrsequence import numpy as np from scipy.stats import nakagami from scipy.special import j0

%%

m = np.arange(0, 100) signal = gencorrsequence( distobj=nakagami(nu=1), targetacf=np.array(j0(0.1 * np.pi * abs(m))), debug=True) ```

Debug figure Autocorrelation function

Notes

There is no responsibility for the correctness of the results. It may work and it may not - use debug option to check the results.
Examples for different distributions (uniform, exponential, Laplace, Rayleigh, triangle, gamma, lognormal, Nakagami) and four different ACFs are provided in the examples/evaluation/ folder. The evaluate_PDFs.ipynb file used for generation of all the ACF figures in the directory.
It takes about 2-3 seconds to generate a single default-length sequence.
The example of code repeatability is provided in the examples/nakagami_example.ipynb notebook as extension of Nakagami distribution example. The sequence is generated 20 times with ACFs as follows.

Autocorrelation function

Installation

Use: pip install generate_corr_sequence

References

The algorithm is mainly based on the following papers:

Filho, José Cândido Silveira Santos, and Michael Daoud Yacoub. "Coloring Non-Gaussian Sequences." IEEE Transactions on Signal Processing, vol. 56, no. 12, 2008, p. 6.
Liu, Bede, et al. "Generation of a Random Sequence Having a Jointly Specified Marginal Distribution and Autocovariance." IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-30, no. 6, 1982, p. 11.

Contributors

Dima Bykhovsky, Netanel Tochilovsky, Alexander Rudyak

License

This project is licensed under the MIT license.

Todo

custom (non-scipy) distributions support
higher-speed algorithm for Gaussian distributions
fix lognormal distribution problem for oscillatory ACFs

Owner

Name: Dima Bykhovsky
Login: bykhov
Kind: user

Website: https://en.sce.ac.il/faculty/bykhovsky
Repositories: 1
Profile: https://github.com/bykhov

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Generate stationary correlated time-series
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Dima
    family-names: Bykhovsky
    orcid: 'https://orcid.org/0000-0003-4468-7791'
  - given-names: Netanel
    family-names: Tochilovsky
  - given-names: Alexander
    family-names: Rudyak
    orcid: 'https://orcid.org/0009-0005-0985-3434'
repository-code: 'https://github.com/bykhov/generate_corr_sequence'
abstract: >-
  This Python function creates a time-series (discrete-time
  random process) with a specific autocorrelation function
  (ACF) and continuous probability distribution, e.g with
  predefined probability density function (PDF).
keywords:
  - Python
  - time-series
  - simulation
  - wss
  - autocorrelation
  - random-process
  - autocovariance
license: MIT
preferred-citation:
  type: conference-paper
  authors:
  - family-names: "Bykhovsky"
    given-names: "Dima"
  - family-names: "Tochilovsky"
    given-names: "Netanel"
  - family-names: "Rudyak"
    given-names: "Alexander"
  doi: "10.1109/ICECET58911.2023.10389427"
  journal: "2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)"
  month: 11
  start: 1 # First page number
  end: 6 # Last page number
  title: "Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function"
  year: 2023

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Total packages: 1
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- pypi 13 last-month

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Total versions: 5
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pypi.org: generate-corr-sequence

Generate correlated random samples

Homepage: https://github.com/bykhov/generate_corr_sequence
Documentation: https://generate-corr-sequence.readthedocs.io/
License: Copyright (c) 2023 The Python Packaging Authority Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Latest release: 0.1.1
published almost 3 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 13 Last month

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Dependent packages count: 6.6%

Average: 18.7%

Downloads: 18.8%

Dependent repos count: 30.6%

Maintainers (3)

AlexRudyak dima373 Nati-Toch