论文标题
反合式：长序列起源原状矩阵预测针对跨应用方案的空间变压器预测
ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario
论文作者
论文摘要
OD区域对之间的原点污染（OD）矩阵记录方向流数据。矩阵中复杂的时空依赖性使OD矩阵预测（ODMF）问题不仅可以棘手，而且是非平凡的。但是，大多数相关方法都是为在特定的应用程序方案中预测非常短的序列时间序列而设计的，在特定的应用程序场景中，该方法无法满足方案和预测实用应用长度的差异要求。为了解决这些问题，我们提出了一个名为Odformer的类似变压器的模型，具有两个显着特征：（i）新型的OD注意机制，该机制捕获了相同起源（目的地）的OD对之间的特殊空间依赖性，可大大提高该模型与2D-GCN相结合后的互相关方案的能力。 （ii）一个时期的自我注意事项，可以有效地预测长序列od矩阵序列，同时适应不同情况下的周期性差异。在三个应用程序背景（即运输流量，IP骨干网络流量，人群流）中进行的慷慨实验表明，我们的方法的表现优于最新方法。
Origin-Destination (OD) matrices record directional flow data between pairs of OD regions. The intricate spatiotemporal dependency in the matrices makes the OD matrix forecasting (ODMF) problem not only intractable but also non-trivial. However, most of the related methods are designed for very short sequence time series forecasting in specific application scenarios, which cannot meet the requirements of the variation in scenarios and forecasting length of practical applications. To address these issues, we propose a Transformer-like model named ODformer, with two salient characteristics: (i) the novel OD Attention mechanism, which captures special spatial dependencies between OD pairs of the same origin (destination), greatly improves the ability of the model to predict cross-application scenarios after combining with 2D-GCN that captures spatial dependencies between OD regions. (ii) a PeriodSparse Self-attention that effectively forecasts long sequence OD matrix series while adapting to the periodic differences in different scenarios. Generous experiments in three application backgrounds (i.e., transportation traffic, IP backbone network traffic, crowd flow) show our method outperforms the state-of-the-art methods.
