用Transformer统一建模时空依赖，交通预测效果提升显著
T-Graphormer: Using Transformers for Spatiotemporal Forecasting
- 在Graphormer中加入时间编码，实现时空联合建模
- 在真实交通数据集上RMSE降低最多20%，MAPE降低10%
- 适合需要高精度时空预测的场景，如城市交通
时空数据无处不在，其预测在诸多领域具有重要应用。然而，复杂的跨组件依赖关系和非线性时间动态给传统方法带来挑战。现有方法通常分别处理时空两个维度。本文提出时序图变换器（T-Graphormer），一种基于Transformer的模型，可同时建模时空相关性。通过在Graphormer架构中引入时间编码，每个节点可关注图序列中所有其他节点，使模型在极少预设归纳偏置的情况下学习丰富的时空模式。我们在真实世界交通预测基准数据集上验证了T-Graphormer的有效性。相比最先进方法，其在RMSE上最高降低20%，在MAPE上最高降低10%。
原文摘要 · Abstract (English)
Spatiotemporal data is ubiquitous, and forecasting it has important applications in many domains. However, its complex cross-component dependencies and non-linear temporal dynamics can be challenging for traditional techniques. Existing methods address this by learning the two dimensions separately. Here, we introduce Temporal Graphormer (T-Graphormer), a Transformer-based approach capable of modelling spatiotemporal correlations simultaneously. By adding temporal encodings in the Graphormer architecture, each node attends to all other tokens within the graph sequence, enabling the model to learn rich spacetime patterns with minimal predefined inductive biases. We show the effectiveness of T-Graphormer on real-world traffic prediction benchmark datasets. Compared to state-of-the-art methods, T-Graphormer reduces root mean squared error (RMSE) and mean absolute percentage error (MAPE) by up to 20% and 10%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。