Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction
This work addresses the challenge of accurate long-term forecasting for urban applications like accident prevention and resource allocation, representing an incremental improvement in spatial-temporal prediction methods.
The paper tackles the problem of error-sensitive long-term spatial-temporal prediction in urban data mining by proposing a Dynamic Switch-Attention Network (DSAN) with a Multi-Space Attention mechanism, which filters irrelevant noises and reduces error propagation, achieving superior performance in both short-term and long-term predictions.
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.