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

Zhang, Chaoyun and Fiore, Marco and Murray, Iain and Patras, Paul (2021) CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting. In: The 35th AAAI Conference on Artificial Intelligence (AAAI 2021), 2-9 February 2021, Online (previously Vancouver, Canada).

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This paper introduces CloudLSTM, a new branch of recurrentneural 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.

Item Type: Conference or Workshop Papers (Paper)
Depositing User: Marco Fiore
Date Deposited: 11 Dec 2020 06:36
Last Modified: 11 Dec 2020 06:36

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