Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Statsforecast | 3,339 | 26 | 3 months ago | 27 | August 23, 2023 | 85 | apache-2.0 | Python | ||
Lightning ⚡️ fast forecasting with statistical and econometric models. | ||||||||||
Pyflux | 2,054 | 15 | 2 | 6 months ago | 36 | November 21, 2017 | 93 | bsd-3-clause | Python | |
Open source time series library for Python | ||||||||||
Awesome_time_series_in_python | 1,811 | a year ago | 4 | |||||||
This curated list contains python packages for time series analysis | ||||||||||
Breakoutdetection | 706 | 7 years ago | 23 | gpl-2.0 | C++ | |||||
Breakout Detection via Robust E-Statistics | ||||||||||
Fecon235 | 622 | 5 years ago | 3 | other | Jupyter Notebook | |||||
Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics | ||||||||||
Pyriemann | 574 | 7 | 11 | 22 days ago | 11 | July 17, 2022 | 9 | bsd-3-clause | Python | |
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python | ||||||||||
Collapse | 556 | 19 | 3 months ago | 38 | December 07, 2023 | 5 | other | C | ||
Advanced and Fast Data Transformation in R | ||||||||||
Hierarchicalforecast | 473 | 4 | 3 months ago | 15 | November 21, 2023 | 39 | apache-2.0 | Python | ||
Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods. | ||||||||||
Pydlm | 467 | 1 | 1 | 7 months ago | 14 | December 19, 2018 | 38 | bsd-3-clause | Python | |
A python library for Bayesian time series modeling | ||||||||||
Zigzag | 349 | 4 | 9 months ago | 9 | August 06, 2022 | 15 | bsd-3-clause | Jupyter Notebook | ||
Python library for identifying the peaks and valleys of a time series. |