TSIClient
A python package to extract data from Azure Time Series Insights (TSI). (by RaaLabs)
pytorch-forecasting
Time series forecasting with PyTorch (by jdb78)
| TSIClient | pytorch-forecasting | |
|---|---|---|
| 1 | 9 | |
| 6 | 3,611 | |
| - | - | |
| 7.0 | 8.6 | |
| 7 months ago | 7 days ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of TSIClient.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-31.
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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', ], )
pytorch-forecasting
Posts with mentions or reviews of pytorch-forecasting.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-14.
- FLaNK Stack Weekly for 14 Aug 2023
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Pytorch Lstm
Source: Conversation with Bing, 4/5/2023 (1) jdb78/pytorch-forecasting: Time series forecasting with PyTorch - GitHub. https://github.com/jdb78/pytorch-forecasting. (2) Time Series Prediction with LSTM Using PyTorch - Colaboratory. https://colab.research.google.com/github/dlmacedo/starter-academic/blob/master/content/courses/deeplearning/notebooks/pytorch/Time_Series_Prediction_with_LSTM_Using_PyTorch.ipynb. (3) time-series-classification · GitHub Topics · GitHub. https://github.com/topics/time-series-classification. (4) PyTorch: Dataloader for time series task - Stack Overflow. https://stackoverflow.com/questions/57893415/pytorch-dataloader-for-time-series-task.
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[D] What is the best approach to create embeddings for time series with additional historical events to use with Transformers model?
Temporal fusion transformer https://github.com/jdb78/pytorch-forecasting
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LSTM/CNN architectures for time series forecasting[Discussion]
Pytorch-forecasting
- Can someone help me with this? It's been days that i struggle with this problem, Forecasting w DeepAR
- Can someone help me with this? it's been days that i struggle with this problem
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[P] Beware of false (FB-)Prophets: Introducing the fastest implementation of auto ARIMA [ever].
To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast
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When to go for an 'easy' time-series model vs. using a complex deep learning model (when having experience with the latter)
I'm a data trainee at this organisation. I wrote my master thesis about using an event clustering mechanism to enrich an existing dataset to improve short-term demand predictions, using Pytorch Forecasting using the temporal fusion transformer component, and LightGBM (and compare the models with and w/o the event feature, so 4 runs in total).
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A python library for easy manipulation and forecasting of time series.
Darts is a pretty nice one. I've recently been using pytorch-forecasting for larger models like the Temporal Fusion Transformer. https://github.com/jdb78/pytorch-forecasting
What are some alternatives?
When comparing TSIClient and pytorch-forecasting you can also consider the following projects:
azure-devops-pypackage
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
tslearn - The machine learning toolkit for time series analysis in Python
snntorch - Deep and online learning with spiking neural networks in Python
Informer2020 - The GitHub repository for the paper "Informer" accepted by AAAI 2021.
nixtla - Python SDK for TimeGPT, a foundational time series model
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
TSIClient vs azure-devops-pypackage
pytorch-forecasting vs darts
pytorch-forecasting vs Prophet
pytorch-forecasting vs neuralforecast
pytorch-forecasting vs Lime-For-Time
pytorch-forecasting vs pytorch-lightning
pytorch-forecasting vs tslearn
pytorch-forecasting vs snntorch
pytorch-forecasting vs Informer2020
pytorch-forecasting vs nixtla
pytorch-forecasting vs DeepSpeed
pytorch-forecasting vs uncertainty-baselines