Metadata-Version: 2.4
Name: scsgmm
Version: 0.1.2
Summary: Sequential Conditional Sampling from GMMs
Project-URL: Homepage, https://github.com/MutaharChalmers/scsgmm
Author-email: Mutahar Chalmers <mutahar.chalmers@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Mutahar Chalmers
        
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License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.11
Requires-Dist: numpy>=1.26.4
Requires-Dist: pandas>=2
Requires-Dist: scikit-learn>=1.6
Requires-Dist: tqdm>=4.66.2
Description-Content-Type: text/markdown

# scsgmm
Sequential Conditional Sampling from Gaussian Mixed Models (SCS-GMM) is a method to fit simple parametric models to univariate and multivariate time series and generate synthetic realisations. The method was inspired by Sharma et al (1997) Streamflow simulation: A nonparametric approach [https://doi.org/10.1029/96WR02839], and originally implemented using KDEs in the package `scskde`. The next logical step was to try an analogous parametric approach, the simplest of which is to replace the KDEs with GMMs. The approach supports
- lag orders >= 1
- arbitrary seasonality
- vector-valued processes
- exogenous forcing
- arbitrary dependence structure
