# Forecat
Forecat is a Python package that provides a Keras-based forecast model builder.
[](https://www.python.org/)
[](https://keras.io/)
It provides the arquitectures for CNN, LSTM, and Encoder Decoder, and even from imagery UNET.
Any suggestions and tips are welcome.
Use this to fastly have your forecast models ready to use!
## Usage
To use Forecat, follow these steps:
```bash
pip install forecat
```
Since Forecat is based on keras-core you can choose which backend to use, otherwise it will default to tensorflow.
To change backend change the ```KERAS-BACKEND``` enviromental variable. Follow [this](https://keras.io/keras_core/#configuring-your-backend).
To get an arquiteture you only need to have a simple configuration and call the module:
```python
import forecat
# The input arguments
input_args = {
"X_timeseries": 168,
"Y_timeseries": 24,
"n_features_train": 18,
"n_features_predict": 1,
}
# This is make a model with shapes:
# input_shape = (N, 168, 18)
# output_shape = (N, 24, 1)
forearch = forecat.CNNArch(**input_args)
# Now for Vanilla and Stacked CNN:
architecture_args = {}
VanillaCNN = forearch.architecture(**architecture_args)
architecture_args = {"block_repetition": 2}
StackedCNN = forearch.architecture(**architecture_args)
# Keras Models ready to use:
VanillaCNN.summary()
StackedCNN.summary()
```
## [Contribution](CONTRIBUTING.md)
Contributions to Forecat are welcome! If you find any issues or have suggestions for improvement, please feel free to contribute. Make sure to update tests as appropriate and follow the contribution guidelines.
## License
Forecat is licensed under the MIT License, which allows you to use, modify, and distribute the package according to the terms of the license. For more details, please refer to the [LICENSE](LICENSE) file.
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"description": "# Forecat\n\nForecat is a Python package that provides a Keras-based forecast model builder.\n\n[](https://www.python.org/)\n[](https://keras.io/)\n\nIt provides the arquitectures for CNN, LSTM, and Encoder Decoder, and even from imagery UNET.\nAny suggestions and tips are welcome.\nUse this to fastly have your forecast models ready to use!\n\n\n## Usage\n\nTo use Forecat, follow these steps:\n\n```bash\n pip install forecat\n```\n\nSince Forecat is based on keras-core you can choose which backend to use, otherwise it will default to tensorflow.\nTo change backend change the ```KERAS-BACKEND``` enviromental variable. Follow [this](https://keras.io/keras_core/#configuring-your-backend).\n\nTo get an arquiteture you only need to have a simple configuration and call the module:\n\n```python\nimport forecat\n\n# The input arguments\ninput_args = {\n \"X_timeseries\": 168,\n \"Y_timeseries\": 24,\n \"n_features_train\": 18,\n \"n_features_predict\": 1,\n}\n# This is make a model with shapes:\n # input_shape = (N, 168, 18)\n # output_shape = (N, 24, 1)\n\nforearch = forecat.CNNArch(**input_args)\n\n# Now for Vanilla and Stacked CNN:\narchitecture_args = {}\nVanillaCNN = forearch.architecture(**architecture_args)\n\narchitecture_args = {\"block_repetition\": 2}\nStackedCNN = forearch.architecture(**architecture_args)\n\n# Keras Models ready to use:\nVanillaCNN.summary()\nStackedCNN.summary()\n\n\n```\n\n## [Contribution](CONTRIBUTING.md)\n\nContributions to Forecat are welcome! If you find any issues or have suggestions for improvement, please feel free to contribute. Make sure to update tests as appropriate and follow the contribution guidelines.\n\n## License\n\nForecat is licensed under the MIT License, which allows you to use, modify, and distribute the package according to the terms of the license. For more details, please refer to the [LICENSE](LICENSE) file.\n",
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