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Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile
IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date : 2020-10-01 , DOI: 10.1109/lra.2020.3004785
Ziyue Li , Hao Yan , Chen Zhang , Fugee Tsung

Spatiotemporal data are very common in many applications, such as manufacturing systems and transportation systems. Given the intrinsic complex spatial and temporal correlations of such data, short-term and long-term prediction for spatiotemporal data is often very challenging. Most of the traditional statistical models fail to preserve innate features in data alongside their complex correlations. In this paper, we focus on a tensor-based prediction method and propose several practical techniques to improve both long-term and short-term prediction accuracy. For long-term prediction, we propose the “tensor decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)” model, and an effective way to update prediction in real-time; For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplification and ensure accuracy. A case study based on the metro passenger flow data is conducted to demonstrate the improved performance.

中文翻译:

客流剖面的长短期时空张量预测

时空数据在许多应用中非常常见,例如制造系统和运输系统。鉴于此类数据固有的复杂空间和时间相关性,时空数据的短期和长期预测通常非常具有挑战性。大多数传统的统计模型未能在数据中保留其复杂的相关性的先天特征。在本文中,我们专注于基于张量的预测方法,并提出了几种实用技术来提高长期和短期预测的准确性。对于长期预测,我们提出了“张量分解+二维自回归移动平均(2D-ARMA)”模型,是一种实时更新预测的有效方法;对于短期预测,我们建议基于张量聚类进行张量补全,以避免过度简化并确保准确性。进行了基于地铁客流数据的案例研究,以证明改进后的性能。
更新日期:2020-10-01
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