Mobility Prediction with Missing Locations Based on Modified Markov Model for Wireless Users

Junyao Guo, Lu Liu, Sihai Zhang, Jinkang Zhu
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引用次数: 1

Abstract

Mobility prediction is an interesting topic attracting many researchers and both prediction theory and models are explored in the existing literature. The entropy metric to evaluate the mobility predictability of individuals gives a theoretical upper bound and lower bound of prediction probability, although the achieved accuracies of users with the same predictability vary. In this work, we investigate the missing locations phenomenon which means the users visit new locations in the testing set. The major difference of theoretical bound between with and without missing locations are found, which shows that users without missing locations are easier to predict. After discussing the impact of missing locations on the prediction accuracy, a modified Markov chain prediction model is proposed to deal with the presence of missing positions. Finally, the correlation between accuracy and predictability can be modeled as the Gaussian distribution and the standard deviation modeled with missing locations can be modeled as double Gaussian function, while that without missing locations can be modeled as the third-order polynomial function.
基于改进马尔可夫模型的无线用户缺失位置移动预测
流动性预测是一个有趣的研究课题,现有文献对预测理论和模型进行了探讨。尽管具有相同可预测性的用户的实现精度各不相同,但用于评估个人移动性可预测性的熵度量给出了预测概率的理论上限和下限。在这项工作中,我们研究了缺失位置现象,即用户访问测试集中的新位置。发现有缺失位置和没有缺失位置的理论边界存在较大差异,说明没有缺失位置的用户更容易预测。在讨论了缺失位置对预测精度的影响后,提出了一种改进的马尔可夫链预测模型来处理缺失位置的存在。最后,准确度与可预测性之间的相关性可建模为高斯分布,有缺失位置的标准差可建模为双高斯函数,无缺失位置的标准差可建模为三阶多项式函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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