SDV
tsfresh
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| SDV | tsfresh | |
|---|---|---|
| 59 | 4 | |
| 2,117 | 8,076 | |
| 14.0% | 0.8% | |
| 9.3 | 5.9 | |
| 6 days ago | 3 days ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SDV
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Synthetic data generation for tabular data
Can someone help me understand the licensing of this?
https://github.com/sdv-dev/SDV/blob/main/LICENSE
It was MIT licensed up until 2022 where it was changed to what it is now, where they say that it will become MIT again 4 years after release... but is that from when the license was changed or the first release of the software in GitHub?
- SDV: NEW Data - star count:1441.0
- FLaNK Stack Weekly for 30 April 2023
- SDV: NEW Data - star count:1196.0
tsfresh
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For deep learning practitioners in industry, is the workflow always this annoying? [D]
This is definitely a good thing to try for time-series; you can automate your feature extraction too (eg using https://github.com/blue-yonder/tsfresh ).
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[D] Incorporating external data in LSTM models for sales forecasting in e-commerce
don't forget your feature engineering -> https://github.com/blue-yonder/tsfresh
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[R] Approach to identify clusters on a time series
Rather than the exact clustering algorithm, I think the main issue here is the feature extraction for the clustering. https://github.com/blue-yonder/tsfresh might be useful for that.
- Automatic time series feature extraction based on scalable hypothesis tests
What are some alternatives?
CTGAN - Conditional GAN for generating synthetic tabular data.
tsflex - Flexible time series feature extraction & processing
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python
machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.
Deep_Learning_Machine_Learning_Stock - Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
Copulas - A library to model multivariate data using copulas.
Time-Series-Transformer - A data preprocessing package for time series data. Design for machine learning and deep learning.
EigenGAN-Tensorflow - EigenGAN: Layer-Wise Eigen-Learning for GANs (ICCV 2021)
darts - A python library for user-friendly forecasting and anomaly detection on time series.
tsfel - An intuitive library to extract features from time series.