  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.
