  Spatiotemporal time series nowcasting should preserve temporal and spatial
dynamics in the sense that generated new sequences from models respect the
covariance relationship from history. Conventional feature extractors are built
with deep convolutional neural networks (CNN). However, CNN models have limits
to image-like applications where data can be formed with high-dimensional
arrays. In contrast, applications in social networks, road traffic, physics,
and chemical property prediction where data features can be organized with
nodes and edges of graphs. Transformer architecture is an emerging method for
predictive models, bringing high accuracy and efficiency due to attention
mechanism design. This paper proposes a spatiotemporal model, namely GTrans,
that transforms data features into graph embeddings and predicts temporal
dynamics with a transformer model. According to our experiments, we demonstrate
that GTrans can model spatial and temporal dynamics and nowcasts extreme events
for datasets. Furthermore, in all the experiments, GTrans can achieve the
highest F1 and F2 scores in binary-class prediction tests than the baseline
models.
