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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -13,7 +13,7 @@
[![PyPi](https://img.shields.io/pypi/v/neuralforecast?color=blue)](https://pypi.org/project/neuralforecast/)
[![conda-nixtla](https://img.shields.io/conda/vn/conda-forge/neuralforecast?color=seagreen&label=conda)](https://anaconda.org/conda-forge/neuralforecast)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/Nixtla/neuralforecast/blob/main/LICENSE)
[![docs](https://img.shields.io/website-up-down-green-red/http/nixtla.github.io/neuralforecast.svg?label=docs)](https://nixtla.github.io/neuralforecast/)
[![docs](https://img.shields.io/website-up-down-green-red/https/nixtlaverse.nixtla.io/neuralforecast.svg?label=docs)](https://nixtlaverse.nixtla.io/neuralforecast/)
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-11-orange.svg?style=flat-square)](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->
Expand Down Expand Up @@ -132,7 +132,7 @@ If you enjoy or benefit from using these Python implementations, a citation to t

## Contributors ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/en/reference/emoji-key/)):
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
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2 changes: 1 addition & 1 deletion neuralforecast/models/dlinear.py
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Expand Up @@ -85,7 +85,7 @@ class DLinear(BaseModel):
**trainer_kwargs (int): keyword trainer arguments inherited from [PyTorch Lightning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).

References:
- [Zeng, Ailing, et al. "Are transformers effective for time series forecasting?." Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 9. 2023."](https://ojs.aaai.org/index.php/AAAI/article/view/27695)
- [Zeng, Ailing, et al. "Are transformers effective for time series forecasting?." Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 9. 2023."](https://ojs.aaai.org/index.php/AAAI/article/view/26317)
"""

# Class attributes
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2 changes: 1 addition & 1 deletion neuralforecast/models/nlinear.py
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Expand Up @@ -49,7 +49,7 @@ class NLinear(BaseModel):
**trainer_kwargs (int): keyword trainer arguments inherited from [PyTorch Lightning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).

References:
- [Zeng, Ailing, et al. "Are transformers effective for time series forecasting?." Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 9. 2023."](https://ojs.aaai.org/index.php/AAAI/article/view/27695)
- [Zeng, Ailing, et al. "Are transformers effective for time series forecasting?." Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 9. 2023."](https://ojs.aaai.org/index.php/AAAI/article/view/26317)
"""

# Class attributes
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