| Name | Version | Summary | date |
| antiCPy |
0.0.8.post1 |
A package that provides tools to estimate resilience and noise level of a system as well as extrapolate possible transition times. |
2024-04-12 06:45:16 |
| fedot-ind |
0.4.1.2 |
Automated machine learning framework for time series analysis |
2024-03-07 12:51:52 |
| exofop |
0.0.2 |
Generic light curve object |
2024-02-28 17:52:29 |
| PyRQA |
8.1.0 |
Recurrence analysis in a massively parallel manner using the OpenCL framework. |
2024-02-17 20:26:07 |
| spm1d |
0.4.28 |
One-Dimensional Statistical Parametric Mapping |
2024-02-13 03:14:29 |
| rft1d |
0.2.3 |
'One-Dimensional Random Field Theory |
2024-01-14 01:54:25 |
| AdvEMDpy |
0.0.17 |
Advanced Empirical Mode Decomposition package with numerous extensions to various aspects of core algorithm. |
2024-01-06 15:06:52 |
| roerich |
0.6.0 |
Change point detection. |
2023-07-17 10:48:39 |
| power1d |
0.1.12 |
Numerical Power Estimates for One-Dimensional Continua |
2023-07-17 04:38:41 |
| time-series-binder |
1.0.4 |
Time Series Binder is a Python library for time series analysis and forecasting. It offers a comprehensive set of tools and models, including Pandas integration, statistical methods, neural networks with Keras, and the NeuralProphet library. With Time Series Binder, you can easily manipulate, visualize, and predict time series data, making it an essential toolkit for researchers and analysts. |
2023-05-30 03:45:28 |
| neurodsp |
2.2.1 |
Digital signal processing for neural time series. |
2022-09-27 00:10:40 |