论文标题
关于平稳性，强大的基准和基准在运输预测问题中的重要性
On the importance of stationarity, strong baselines and benchmarks in transport prediction problems
论文作者
论文摘要
在过去的几年中，运输界在新的深度学习方法上进行了大量的研究贡献。这些贡献倾向于强调空间相关性的建模，同时忽略了人类流动性模式的相当稳定和经常性的性质。在这篇简短的论文中，我们表明一种基于平均每周模式和线性回归的天真基线方法可以与许多最先进的深度学习方法获得可比的结果，用于运输时时空预测，甚至在几个数据集中胜过它们，从而在几个数据集上胜过它们，从而使其与spatial corelelials corelelel of Spatatial corelent of Spatatial corel的重要性相比。此外，我们建立了9种不同的参考基准，可用于比较时空预测的新方法，并提供有关最佳实践和该领域采取方向的讨论。
Over the last years, the transportation community has witnessed a tremendous amount of research contributions on new deep learning approaches for spatio-temporal forecasting. These contributions tend to emphasize the modeling of spatial correlations, while neglecting the fairly stable and recurrent nature of human mobility patterns. In this short paper, we show that a naive baseline method based on the average weekly pattern and linear regression can achieve comparable results to many state-of-the-art deep learning approaches for spatio-temporal forecasting in transportation, or even outperform them on several datasets, thus contrasting the importance of stationarity and recurrent patterns in the data with the importance of spatial correlations. Furthermore, we establish 9 different reference benchmarks that can be used to compare new approaches for spatio-temporal forecasting, and provide a discussion on best practices and the direction that the field is taking.
