Extralonger: Toward a Unified Perspective of Spatial-Temporal Factors for Extra-Long-Term Traffic Forecasting
This addresses the need for longer-term traffic predictions in Intelligent Transportation Systems, offering a novel approach that significantly extends forecasting capabilities beyond current methods.
The paper tackles the problem of limited prediction horizons in traffic forecasting, which typically only extend to a few hours, by proposing Extralonger, a method that unifies temporal and spatial factors, extending the prediction horizon to a week on real-world benchmarks with improved efficiency in training time, inference time, and memory usage.
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.