DOCUMENTATION
Why NeuralForecast
NeuralForecast is a time-series forecasting library with deep learning models.
Why Deep Learning
- Highly Accurate Predictions:
- High capacity shared models across panel data time series.
- Fast and Efficient Models:
- Automatic featurization provided by the networks information processes.
- Fast GPU computations.
NeuralForecast Features
- Easy-to-use state-of-the-art models:
- Dataset, dataloader and evaluation utility.
- Code organization follows Lightning. Pure PyTorch without boilerplate.
- Implementations of high performing forecasting models with minimal entry barriers.
- High Efficiency and low computation costs:
- Fast dataloaders and model optimization.
- Scalable to any hardware without changing the models.
Tutorial 1: Installation and Introduction
Tutorial 2: Time Series DataSets and DataLoaders
Tutorial 3: Model Training and Evaluation
Tutorial 4: Production Deployment
Community
Slack, twitter, something else
3 maintainers
Release files for neuralforecast 0.0.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| neuralforecast-0.0.5.tar.gz | 94.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| neuralforecast-0.0.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 212.8 kB
Release files / neuralforecast-0.0.5.tar.gz
| Download URL | neuralforecast-0.0.5.tar.gz |
|---|---|
| Size | 94.6 kB |
| Tags | Source |
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Release files / neuralforecast-0.0.5-py3-none-any.whl
| Download URL | neuralforecast-0.0.5-py3-none-any.whl |
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| Size | 118.2 kB |
| Tags | Python 3 |
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