论文标题
一个时空的斑点销售框架，用于城市交通预测
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction
论文作者
论文摘要
时空预测是一个开放的研究领域，其兴趣呈指数增长。在这项工作中，我们着重于创建一个复杂的深层神经框架，以预测时空流量，并且性能相对较好，并且表明在几个时空条件下可以适应，同时又易于理解和解释。我们的建议基于一个可解释的基于注意力的神经网络，在该网络中，将几个模块组合在一起，以捕获关键时空时间序列组件。通过广泛的实验，我们展示了方法的结果比其他最先进的替代方案的结果如何稳定且更好。
Spatio-temporal forecasting is an open research field whose interest is growing exponentially. In this work we focus on creating a complex deep neural framework for spatio-temporal traffic forecasting with comparatively very good performance and that shows to be adaptable over several spatio-temporal conditions while remaining easy to understand and interpret. Our proposal is based on an interpretable attention-based neural network in which several modules are combined in order to capture key spatio-temporal time series components. Through extensive experimentation, we show how the results of our approach are stable and better than those of other state-of-the-art alternatives.
