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neuralforecast 1.7.5

Time series forecasting suite using deep learning models

07-27-2023 216 words 2 minutes 0 views

Contents

Time series forecasting suite using deep learning models

Stars: 2997, Watchers: 2997, Forks: 346, Open Issues: 114

The Nixtla/neuralforecast repo was created 3 years ago and the last code push was Yesterday.
The project is very popular with an impressive 2997 github stars!

How to Install neuralforecast

You can install neuralforecast using pip

pip install neuralforecast

or add it to a project with poetry

poetry add neuralforecast

Package Details

Author: Nixtla
License: Apache Software License 2.0
Homepage: https://github.com/Nixtla/neuralforecast/
PyPi:: https://pypi.org/project/neuralforecast/
GitHub Repo:: https://github.com/Nixtla/neuralforecast

Classifiers

No neuralforecast pypi packages just yet.

Errors

A list of common neuralforecast errors.

Code Examples

Here are some neuralforecast code examples and snippets.

GitHub Issues

The neuralforecast package has 114 open issues on GitHub

Resources on Temporal Normalization
'pydantic.fields' deprecated
How to interpret the trained model?
v1.6.0 conda package is broken
[FEAT] Unified DeepAR decoder with common.MLP.
Code stuck on "initalizing ddp" when using more than one gpu on neuralforecast AutoTFT, AutoNHITs
Predictions using missing value input (available_mask==0) and scalers ('minmax', 'robust')
Add monitor parameter for early stopping
Feature/insample test
Can't disable logging when using predict(...) method for a model.
[TimesNet]
[Recurrent] validation_step does not scale back the loss for point losses
Is there any plan to put TiDE algorithm into neuralforecast

See more issues on GitHub

Related Packages & Articles

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).