PY-SSA-LIB PACKAGE
Intro
Welcome to the page of the py-ssa-lib package! This package contains python implementations of the Singular Spectrum Analysis(SSA) and Multichannel Singular Spectrum Analysis(MSSA).
It can be used for the time series analysis and forecasting.
Please, take a look at the guides for SSA and MSSA which are available in the corresponding directory!
Mathematical Background
The Wiki for the py-ssa-lib package is now available and will be periodically updated. It contains some theoretical background about the MSSA and SSA. The API documentation is available here.
Updates
NEW: Version 2.0.x is now available!
- Simplified interface
- Improved visualization tools
- Extended SVD choice
- Enabled various weighting schemes for channels of the MSSA
- API documentation ...
Installation
$ python -m pip install py-ssa-lib
Requirements
The classes in the py-ssa-lib rely on the numpy, scipy, sklearn, pandas and matplotlib libraries.
Similar Python Packages
Before the development of the py-ssa-lib I searched for the Python packages which implement both MSSA and SSA, and found only few decent packages with the similar functionality:
(Arguably) the best functionality is provided by the RSSA-package in R (https://github.com/asl/rssa).
List of the Core Packages
- NumPy https://numpy.org/
- SciPy https://scipy.org/
- Jupyter Lab https://jupyterlab.readthedocs.io/en/latest/index.html
- Scikit-learn https://scikit-learn.org/stable/
- Matplotlib https://matplotlib.org/
Literature about SSA and MSSA
- https://www.kaggle.com/code/jdarcy/introducing-ssa-for-time-series-decomposition/notebook#2.-Introducing-the-SSA-Method
- https://link.springer.com/book/10.1007/978-3-642-34913-3
- https://link.springer.com/book/10.1007/978-3-662-57380-8
- https://www.gistatgroup.com/gus/mssa2.pdf
Citation
If you find this package useful, please, cite:
Konstantin Ibadullaev, https://github.com/K-Ibadullaev/py_ssa-lib/
(This file and the citation format will change over time.)
Acknowledgements
This package is developed as a part of the research project "Intelligent Geosystems" (100693905) supported by ESF funding
1 maintainer
Metadata
1 maintainer
Release files for py-ssa-lib 2.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| py_ssa_lib-2.0.0.tar.gz | 14.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| py_ssa_lib-2.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 28.1 kB
Release files / py_ssa_lib-2.0.0.tar.gz
| Download URL | py_ssa_lib-2.0.0.tar.gz |
|---|---|
| Size | 14.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
40f935f257ac8534aa3d25324c5cae5ca70428875498b8b893f5cbfd78d55c70
|
|
BLAKE2b-256 checksum How to use checksums |
9cf1bab3b407733ce8daf64963aed5a29a4c23c5936653022c0e55f3e51d6dc4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|
Release files / py_ssa_lib-2.0.0-py3-none-any.whl
| Download URL | py_ssa_lib-2.0.0-py3-none-any.whl |
|---|---|
| Size | 13.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
2e6b233c9d7ef38dde0a65b2e78804bbf81b534f072ecee2605bb7ba0eb4029f
|
|
BLAKE2b-256 checksum How to use checksums |
92a83ac43127548f43b0b10244c9a0e6a5292df190e732ef312b598ae888e5b2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
|
