gluonts
pytorch-forecasting
| gluonts | pytorch-forecasting | |
|---|---|---|
| 4 | 9 | |
| 4,308 | 3,625 | |
| 2.3% | - | |
| 8.7 | 8.6 | |
| 3 days ago | 2 days ago | |
| Python | Python | |
| Apache License 2.0 | 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.
gluonts
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Show HN: Auto Wiki v2 – Turn your codebase into a Wiki now with diagrams
https://github.com/awslabs/gluonts is a great candidate for a sample wiki. It is an OSS lib, not great documentation, very hard to RTFM (unlike, say, sklearn which already has a great wiki), doubtful that awslabs would pay to produce.
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
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[Q] `py.typed` and `.typesafe`
I was looking at [`gluonts`](https://github.com/awslabs/gluonts/tree/dev/src/gluonts/core) source code and I found a `py.typed` file. That is something I always put in my type-annotated modules: it's literally an empty file which denotes that the module is marked for "internal or external use in type checking" [mypy docs](https://mypy.readthedocs.io/en/stable/installed_packages.html?highlight=py.typed#creating-pep-561-compatible-packages). However, I never saw before the `.typesafe` file. What does it denote? Does it have to be used alongside a `py.typed`?
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Cash-flow forecasting
-GluonTS
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?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
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
time-series-transformers-review - A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
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]
sagemaker-python-sdk - A library for training and deploying machine learning models on Amazon SageMaker
tslearn - The machine learning toolkit for time series analysis in Python