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Published September 23, 2021 | Version v1
Conference paper Open

CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting

  • 1. Tencent Lightspeed & Quantum Studios
  • 2. IMDEA Networks Institute
  • 3. The University of Edinburgh


This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic Point-cloud Convolution (DConv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extracts local spatial features from sets of neighboring points that surround different elements of the input. This operator maintains the permutation invariance of sequence-to-sequence learning frameworks, while representing neighboring correlations at each time step -- an important aspect in spatiotemporal predictive learning. The DConv operator resolves the grid-structural data requirements of existing spatiotemporal forecasting models and can be easily plugged into traditional LSTM architectures with sequence-to-sequence learning and attention mechanisms. We apply our proposed architecture to two representative, practical use cases that involve point-cloud streams, i.e., mobile service traffic forecasting and air quality indicator forecasting. Our results, obtained with real-world datasets collected in diverse scenarios for each use case, show that CloudLSTM delivers accurate long-term predictions, outperforming a variety of competitor neural network models.


This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no.101017109 "DAEMON", and from the Cisco University Research Program Fund (grant no. 2019- 197006).



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DAEMON – Network intelligence for aDAptive and sElf-Learning MObile Networks 101017109
European Commission