Metadata-Version: 2.5
Name: ns-sdn
Version: 1.0.0a1
Summary: Non-Stationary Spectral Decomposition Network: a neural state-space model emitting an interpretable time-varying spectral decomposition of economic and financial time series.
Project-URL: Repository, https://github.com/nikhilxsunder/ns-sdn
Project-URL: Documentation, https://nikhilxsunder.github.io/ns-sdn/
Author-email: Nikhil Sunder <nsunder724@gmail.com>, Maikel Leon <mleon@miami.edu>
License: MIT
License-File: AUTHORS.md
License-File: LICENSE
Keywords: econometrics,instantaneous frequency,neural network,pytorch,spectral decomposition,state space,time series
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: <4.0,>=3.12
Requires-Dist: numpy>=1.26
Requires-Dist: torch>=2.2
Provides-Extra: all
Requires-Dist: fedfred; extra == 'all'
Requires-Dist: matplotlib; extra == 'all'
Requires-Dist: pandas; extra == 'all'
Provides-Extra: data
Requires-Dist: fedfred; extra == 'data'
Provides-Extra: pandas
Requires-Dist: pandas; extra == 'pandas'
Provides-Extra: plot
Requires-Dist: matplotlib; extra == 'plot'
Description-Content-Type: text/markdown

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    <img src="https://raw.githubusercontent.com/nikhilxsunder/ns-sdn/main/assets/exported/ns-sdn_banner.png"  alt="NS-SDN Logo">
</div>

## A modern neural architecture in python for decomposing non-stationary time series into interpretable trend and spectral components.

## Coming Soon!
