TSIClient
pytorch-forecasting
TSIClient | pytorch-forecasting | |
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1 | 9 | |
6 | 3,611 | |
- | - | |
7.0 | 8.6 | |
7 months ago | 8 days ago | |
Python | Python | |
MIT License | MIT License |
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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', ], )
pytorch-forecasting
- 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?
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.