  Effective long-term predictions have been increasingly demanded in urban-wise
data mining systems. Many practical applications, such as accident prevention
and resource pre-allocation, require an extended period for preparation.
However, challenges come as long-term prediction is highly error-sensitive,
which becomes more critical when predicting urban-wise phenomena with
complicated and dynamic spatial-temporal correlation. Specifically, since the
amount of valuable correlation is limited, enormous irrelevant features
introduce noises that trigger increased prediction errors. Besides, after each
time step, the errors can traverse through the correlations and reach the
spatial-temporal positions in every future prediction, leading to significant
error propagation. To address these issues, we propose a Dynamic
Switch-Attention Network (DSAN) with a novel Multi-Space Attention (MSA)
mechanism that measures the correlations between inputs and outputs explicitly.
To filter out irrelevant noises and alleviate the error propagation, DSAN
dynamically extracts valuable information by applying self-attention over the
noisy input and bridges each output directly to the purified inputs via
implementing a switch-attention mechanism. Through extensive experiments on two
spatial-temporal prediction tasks, we demonstrate the superior advantage of
DSAN in both short-term and long-term predictions.
