统一时空因素，将交通预测时长扩展至一周。
Extralonger: Toward a Unified Perspective of Spatial-Temporal Factors for Extra-Long-Term Traffic Forecasting
- 借鉴相对论思想，将时空因素融合建模。
- 在真实数据集上实现长达一周的预测，性能显著提升。
- 适合需要超长时预测的智能交通系统研究者。
交通预测在智能交通系统中至关重要，现有方法大多只能预测未来四小时，难以满足实际需求。本文指出，预测时长受限主要源于时空因素的分离，导致模型复杂度高。受爱因斯坦相对论启发，提出Extralonger，通过统一时空因素，显著提升预测能力。该模型在真实世界基准上将预测时长扩展至一周，同时在训练时间、推理时间和内存占用方面均表现出更优效率，为长期及超长期交通预测设定新标准。代码已开源。
原文摘要 · Abstract (English)
Traffic forecasting plays a key role in Intelligent Transportation Systems, and significant strides have been made in this field. However, most existing methods can only predict up to four hours in the future, which doesn't quite meet real-world demands. we identify that the prediction horizon is limited to a few hours mainly due to the separation of temporal and spatial factors, which results in high complexity. Drawing inspiration from Albert Einstein's relativity theory, which suggests space and time are unified and inseparable, we introduce Extralonger, which unifies temporal and spatial factors. Extralonger notably extends the prediction horizon to a week on real-world benchmarks, demonstrating superior efficiency in the training time, inference time, and memory usage. It sets new standards in long-term and extra-long-term scenarios. The code is available at https://github.com/PlanckChang/Extralonger.
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。