The application of time series regression prediction model in automated monitoring

doi:10.13474/j.cnki.11-2246.2021.0196

Abstract

Abstract: In the process of slope automation monitoring, because of the interruption of transmission signal, equipment failure, power supply interruption and sensor replacement, the phenomenon of missing monitoring data will inevitably occur. The lack of monitoring data brings uncertain factors to the subsequent slope stability analysis and prediction, which makes the analysis results deviate. This paper aims at the phenomenon of missing dynamic monitoring, the time series regression prediction model is used to fill in the monitoring data of slope with different missing data rates. The absolute error and root mean square error between the filling value and the real value are used to judge the filling effect of different missing data rates. It is concluded that the model has good effect on missing data with missing rate less than 10%, and has certain practice.

Key words: time series, missing data, filling, absolute error, root mean square error

CLC Number:

LIU Jianjun, WANG Jiawei, CHEN Kun, HAN Sanqi. The application of time series regression prediction model in automated monitoring[J]. Bulletin of Surveying and Mapping, 2021, 0(6): 156-158.

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