  Spatial time series forecasting problems arise in a broad range of
applications, such as environmental and transportation problems. These problems
are challenging because of the existence of specific spatial, short-term and
long-term patterns, and the curse of dimensionality. In this paper, we propose
a deep neural network framework for large-scale spatial time series forecasting
problems. We explicitly designed the neural network architecture for capturing
various types of patterns. In preprocessing, a time series decomposition method
is applied to separately feed short-term, long-term and spatial patterns into
different components of a neural network. A fuzzy clustering method finds
cluster of neighboring time series based on similarity of time series
residuals; as they can be meaningful short-term patterns for spatial time
series. In neural network architecture, each kernel of a multi-kernel
convolution layer is applied to a cluster of time series to extract short-term
features in neighboring areas. The output of convolution layer is concatenated
by trends and followed by convolution-LSTM layer to capture long-term patterns
in larger regional areas. To make a robust prediction when faced with missing
data, an unsupervised pretrained denoising autoencoder reconstructs the output
of the model in a fine-tuning step. The experimental results illustrate the
model outperforms baseline and state of the art models in a traffic flow
prediction dataset.
