Research on high slope deformation prediction model based on MEEMD-ARIMA

This study proposes a high slope prediction method based on the coupling of multi-scale entropy empirical modal decomposition (MEEMD) and autoregressive integral sliding average model (ARIMA). Firstly, the MEEMD algorithm is used to decompose the high slope displacement time series data to obtain multiple intrinsic modal functions (IMFs), and this process can effectively extract the key trend and periodicity information from the complex displacement data, and reduce the noise interference in the data; subsequently, an ARIMA model is established for each IMF component separately to perform the time series prediction, and the ARIMA model is utilized to predict the high slope displacement time series with high accuracy and high speed, and the ARIMA model is used to predict the high slope displacement time series with high accuracy and high speed. The advantages of high prediction accuracy and speed are utilized to accurately extract the internal change characteristics of the observed data, so as to improve the accuracy of the prediction of the observed data; finally, the prediction results of all the IMF components are reconstructed to obtain the final predicted value of the high slope displacement.