Abstract
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 this https URL.
Abstract (translated)
交通流量预测在智能交通系统中扮演着关键角色，这一领域已经取得了显著进展。然而，现有的大多数方法仅能预测未来四小时的交通状况，这并不能完全满足实际需求。我们发现，预测范围受限于数小时的主要原因是时间与空间因素被分开处理，导致了复杂度增加。借鉴阿尔伯特·爱因斯坦的相对论理论，该理论指出空间和时间是统一且不可分割的，我们引入了Extralonger模型，它将时间和空间因素进行了统一处理。Extralonger在真实世界的基准测试中显著延长了预测范围至一周，并在训练时间、推理时间和内存使用方面展现了优越效率。它为长期及超长期场景设定了新的标准。代码可在以下链接获取：[此 https URL]。
URL
https://arxiv.org/abs/2411.00844
