Summary of Extralonger: Toward a Unified Perspective Of Spatial-temporal Factors For Extra-long-term Traffic Forecasting, by Zhiwei Zhang et al.
Extralonger: Toward a Unified Perspective of Spatial-Temporal Factors for Extra-Long-Term Traffic Forecasting
by Zhiwei Zhang, Shaojun E, Fandong Meng, Jie Zhou, Wenjuan Han
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a new approach called Extralonger that tackles the limitations of current traffic forecasting methods, which can only predict up to four hours in advance. By unifying temporal and spatial factors, Extralonger extends the prediction horizon to a week on real-world benchmarks, achieving superior efficiency in training time, inference time, and memory usage. This breakthrough sets new standards for long-term and extra-long-term scenarios, with potential applications in Intelligent Transportation Systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic forecasting is important for safe and efficient transportation. Right now, most methods can only predict what will happen a few hours from now, but real-world demands require predicting up to a week ahead. A team of researchers came up with an innovative solution called Extralonger that unites time and space to make longer-term predictions. This new approach does a great job in terms of training time, processing speed, and memory usage. The code for this breakthrough is available online. |
Keywords
* Artificial intelligence * Inference
