  Short-term demand forecasting models commonly combine convolutional and
recurrent layers to extract complex spatiotemporal patterns in data. Long-term
histories are also used to consider periodicity and seasonality patterns as
time series data. In this study, we propose an efficient architecture,
Temporal-Guided Network (TGNet), which utilizes graph networks and
temporal-guided embedding. Graph networks extract invariant features to
permutations of adjacent regions instead of convolutional layers.
Temporal-guided embedding explicitly learns temporal contexts from training
data and is substituted for the input of long-term histories from days/weeks
ago. TGNet learns an autoregressive model, conditioned on temporal contexts of
forecasting targets from temporal-guided embedding. Finally, our model achieves
competitive performances with other baselines on three spatiotemporal demand
dataset from real-world, but the number of trainable parameters is about 20
times smaller than a state-of-the-art baseline. We also show that
temporal-guided embedding learns temporal contexts as intended and TGNet has
robust forecasting performances even to atypical event situations.
