Abstract
In recent years, geosensor data forecasting has received considerable attention. However, the presence of correlation (i.e. spatial correlation across several sites and time correlation within each site) poses difficulties to accurate forecasting. In this paper, a tensor-based method for geosensor data forecasting is proposed. Specifically, a tensor pattern is first introduced into modelling the geosensor data, which can take advantage of geosensor spatial-temporal information and preserve the multi-way nature of geosensor data, and then a tensor decomposition based algorithm is developed to forecast future values of time series. The proposed approach not only combines and utilizes the multi-mode correlations, but also well extracts the underlying factors in each mode of tensor and mines the multi-dimensional structures of geosensor data. Experimental evaluations on real world geosensor data validate the effectiveness of the proposed methods.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China (61762090, 61262069, 61472346, and 61662086), The Natural Science Foundation of Yunnan Province (2016FA026, 2015FB114), the Project of Innovative Research Team of Yunnan Province, and Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN).
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Zhou, L., Du, G., Xiao, Q., Wang, L. (2018). A Tensor-Based Method for Geosensor Data Forecasting. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_23
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DOI: https://doi.org/10.1007/978-3-319-96893-3_23
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