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.

该论文提出了一种具有多空间注意力机制的动态切换 - 注意力网络（DSAN），用于解决城市智能数据挖掘系统中的长期预测问题。通过在输入和输出之间显式测量关联性并应用自我注意来提取有价值的信息，并通过实现切换 - 注意力机制将输出直接连接到精制的输入来过滤无关噪声和减轻错误传播。在两个空间 - 时间预测任务上进行了广泛的实验，证明了 DSAN 在短期和长期预测中的卓越优势。