This repository is an unofficial pytorch implementation of Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. We reimplement the experiments in the paper based on the MovingMNIST dataset, which is followed by Github. Detailed understanding is available on my Blog.

• Pytorch>=0.4.0
• CPU or GPU
• Other packages can be installed with the following instruction:

pip install requirements.txt

Running the code with the following command, and the '--config' parameter represents different network architectures.

python main.py --config 3x3_16_3x3_32_3x3_64

• Note: In order to reduce the number of parameters and flops, we did not strictly follow the model architecture in the paper, but modified it into unet style structure.

@inproceedings{xingjian2015convolutional,
  title={Convolutional LSTM network: A machine learning approach for precipitation nowcasting},
  author={Xingjian, SHI and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-Kin and Woo, Wang-chun},
  booktitle={Advances in neural information processing systems},
  pages={802--810},
  year={2015}
}