TSIClient
neural_prophet
| TSIClient | neural_prophet | |
|---|---|---|
| 1 | 5 | |
| 6 | 3,644 | |
| - | - | |
| 7.0 | 8.6 | |
| 7 months ago | 14 days ago | |
| Python | Python | |
| MIT License | 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.
TSIClient
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Hosting Python Packages in Azure DevOps
from setuptools import setup, find_packages with open('README.md') as f: long_description = f.read() setup( name = 'animalsounds', # How you named your package folder (TSIClient) packages = ['animalsounds'], # Chose the same as "name" version = '1.0.0', # Start with a small number and increase it with every change you make license='MIT', # Chose a license from here: https://help.github.com/articles/licensing-a-repository long_description=long_description, long_description_content_type='text/markdown', # This is important! author = 'Vivek Raja P S', # Type in your name author_email = '[email protected]', # Type in your E-Mail url = 'https://github.com/Vivek0712/azure-devops-pypackage', # Provide either the link to your github or to your website #download_url = 'https://github.com/RaaLabs/TSIClient/archive/v_0.7.tar.gz', # If you create releases through Github, then this is important keywords = ['Azure', 'DevOps', 'Python'], # Keywords that define your package best packages = find_packages("src", exclude=["test"]), classifiers=[ 'Development Status :: 3 - Alpha', # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package 'Intended Audience :: Developers', # Define that your audience are developers 'Topic :: Software Development :: Build Tools', 'License :: OSI Approved :: MIT License', # Again, pick a license 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.9', ], )
neural_prophet
- Facebook Prophet: library for generating forecasts from any time series data
- Time series analysis of Bitcoin price in Python with fbprophet ?!
- 14 September 2021 - Daily Chat Thread
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[D] Stock prediction using lstm(plz help)
NeuralProphet
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Financial time-series data forecasting - any other tools besides Prophet?
Neural Prophet: https://github.com/ourownstory/neural_prophet
What are some alternatives?
pytorch-forecasting - Time series forecasting with PyTorch
darts - A python library for user-friendly forecasting and anomaly detection on time series.
azure-devops-pypackage
scikit-hts - Hierarchical Time Series Forecasting with a familiar API
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
sysidentpy - A Python Package For System Identification Using NARMAX Models
Informer2020 - The GitHub repository for the paper "Informer" accepted by AAAI 2021.
tslearn - The machine learning toolkit for time series analysis in Python
kafka-crypto-questdb - Using Kafka to track cryptocurrency price trends
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).