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Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting paper by Xingjian Shi
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Paper

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

by Xingjian Shi arxiv/1506.04214
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Q: Quality 60
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The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (F...

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BibTeX 
@misc{arxiv_1506_04214,
  author = {Xingjian Shi},
  title = {Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/1506.04214}},
  note = {Accessed via Free2AITools.}
}
APA Style
Xingjian Shi. (2026). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting [Paper]. Free2AITools. https://arxiv.org/abs/1506.04214

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 0
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Quality (Q) 60

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FNI V2.0 for Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting: Authority (A:0), Popularity (P:0), Recency (R:0), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (F..."

❝ Cite Node 

@article{Shi2026Convolutional,
  title={Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting},
  author={Xingjian Shi},
  journal={arXiv preprint arXiv:1506.04214},
  year={2026}
}

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Xingjian Shi

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1506.04214
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1506.04214
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arxiv
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Xingjian Shi
license
arXiv
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