Abstract
In view of the poor fitting of the traditional GM (1,1) model in the long-term prediction and its failure in data series with high volatility, a grey Markov prediction model is established by combining grey theory with Markov theory. The location of moving objects is predicted by this model, and more accurate prediction results are obtained. Firstly, the GM (1,1) model is constructed by abstracting the problem into a single variable grey system. Then, at one-minute interval, some GPS trajectory data in GeoLife are extracted and substituted into the prediction model, and the model is tested according to the results. Finally, the Markov model is introduced into GM (1,1) model for further experiments. Experiments show that compared with the traditional GM (1,1) model, the grey Markov prediction model has less error in the location prediction of moving objects and improves the accuracy of the prediction.
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Acknowledgment
The work in this paper is partially supported by the National Innovation and Entrepreneurship Training Program for College Students in 2017 under Grant No. 201713235003 and in part supported by the Project of College-level Teaching and Research Fund of City College Wuhan University of Science and Technology in 2017 under Grant No. 2017CYZDJY004.
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Yang, Y., Ke, B. (2019). Research on Location Prediction of Moving Objects Based on Grey Markov Model. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_20
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DOI: https://doi.org/10.1007/978-3-030-26766-7_20
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