论文标题
术语：有效城市流量预测的时间关系建模
TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting
论文作者
论文摘要
鉴于城市流动模式的固有周期性特征，城市流量预测是一项具有挑战性的任务。为了捕获周期性，现有的城市流动预测方法通常是从城市流量序列中提取的亲密，周期和趋势组成部分设计的。但是，这三个组件在预测模型中通常被分别考虑。这些组件尚未完全探索，并同时纳入城市流量预测模型中。我们介绍了一种新颖的城市流程预测建筑，该建筑学期。明确设计了基于变压器的长期关系预测模块，以发现周期性，并使三个组件能够共同建模该模块预测周期性关系，然后将其用于产生预测的城市流量张量。为了衡量预测的周期性关系向量的一致性以及从预测的城市流量张量推断的关系向量，我们提出了一个一致性模块。培训过程中引入了一致性损失，以进一步提高预测性能。通过在三个现实世界数据集上进行的大量实验，我们证明了Trimcast优于多种最新方法。还研究了每个模块在termcast中的有效性。
Urban flow forecasting is a challenging task, given the inherent periodic characteristics of urban flow patterns. To capture the periodicity, existing urban flow prediction approaches are often designed with closeness, period, and trend components extracted from the urban flow sequence. However, these three components are often considered separately in the prediction model. These components have not been fully explored together and simultaneously incorporated in urban flow forecasting models. We introduce a novel urban flow forecasting architecture, TERMCast. A Transformer based long-term relation prediction module is explicitly designed to discover the periodicity and enable the three components to be jointly modeled This module predicts the periodic relation which is then used to yield the predicted urban flow tensor. To measure the consistency of the predicted periodic relation vector and the relation vector inferred from the predicted urban flow tensor, we propose a consistency module. A consistency loss is introduced in the training process to further improve the prediction performance. Through extensive experiments on three real-world datasets, we demonstrate that TERMCast outperforms multiple state-of-the-art methods. The effectiveness of each module in TERMCast has also been investigated.
