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首页> 外文会议>IEEE International Conference on Intelligent Transportation Systems >Traffic prediction, data compression, abnormal data detection and missing data imputation: An integrated study based on the decomposition of traffic time series
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Traffic prediction, data compression, abnormal data detection and missing data imputation: An integrated study based on the decomposition of traffic time series

机译：交通预测，数据压缩，异常数据检测和缺失数据估算：基于交通时间序列分解的综合研究

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This papers discusses the decomposition of road traffic time series and its benefits. The purposes of this paper are trifold. First, we provide an integrated framework for studying traffic prediction, data compression, abnormal data detection and missing data imputation problems, so that the relations between different problems can be revealed. In this part, we summarize several our works in this direction that had been finished in the last decade. Second, we discuss three most popular detrending methods: simple average detrending, principal component analysis (PCA) based detrending, as well as wavelet based detrending, and account for their intrinsic differences. Third, we present a new finding about trend modeling. We show that the detrending based prediction models previously designed for isolated sensor also work well for multiple sensors. Moreover, we define the so called short-term trend and explain why prediction accuracy can be improved at the points belonging to short trends, when the traffic information from multiple sensors is appropriately used. This new finding indicates that the trend modeling is not only a technique to specify the temporal pattern of traffic flow time series but is also related to the spatial relation of traffic flow time series.
机译：本文讨论了道路交通时间序列的分解及其益处。本文的目的是三大。首先，我们为研究流量预测，数据压缩，异常数据检测和缺少数据归档问题提供了一个综合框架，从而可以揭示不同问题之间的关系。在这方面，我们总结了在过去十年完成的这个方向上的几个作品。其次，我们讨论了三种最受欢迎​​的争论方法：简单的平均贬值，主要成分分析（PCA）的基于劣势，以及基于小波的争论，以及其内在差异的算帐。第三，我们展示了一个关于趋势建模的新发现。我们表明，先前为隔离传感器设计的基于劣化的预测模型也适用于多个传感器。此外，我们定义所谓的短期趋势，并解释为什么在适当使用来自多个传感器的交通信息时，可以在属于短趋势的点处改善预测准确度。这个新发现表明趋势建模不仅是指定交通流量时间序列的时间模式的技术，而且还与交通流量时间序列的空间关系有关。

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