## 1 Introduction  
Landslides are one of the many common natural disasters in the world [1; 2]. A frequent occurrence of extreme weather increases the likelihood of landslides. Rapid economic and social development further aggravates the loss of landslide disasters [3; 4]. Therefore, accurate landslide displacement prediction is becoming increasingly important in order to prevent and mitigate the damage caused by landslides [5]. In the process of automated real-time monitoring of landslides, data acquisition and transmission are generally performed using different types of sensors and other electronic devices [6]. Automated monitoring equipment is always in the open-air environment, and most of them inevitably suffer from tear, aging, power loss and other phenomena, all of which can lead to missing monitoring data. In addition, most landslide disasters are located in a relatively harsh geological environment, such as heavy rainfall, hail, dense fog, electromagnetic interference, etc., and the installation and deployment of geological hazard monitoring equipment in open fields will inevitably be affected by the abovementioned harsh environment. Randomness or prolonged interruptions in the operation of the monitoring device can cause the monitoring device to fail to properly send monitoring data to the server, which leads to the problem of missing monitoring time series in the server. Time series forecasting is a valid basis for making accurate discriminations. To enhance the accuracy of landslide prediction, it is necessary to construct a corresponding accurate data completion method. For the problem of completing and predicting missing landslide data, most traditional time series models have focused on models such as regression analysis and exponential smoothing. The problem of incompleteness for missing time series data can be broadly classified as either deletion or padding. Data deletion is used as anomalous data to remove some objective abnormal monitoring data, which is primarily used for anomaly detection and feature analysis, while filling is utilized to find the long-term time series change pattern of monitoring data and to supplement the missing monitoring data. The main methods include missing value filling algorithm based on nearest neighbor method, cyclic neural network, random forest and matrix decomposition, but more data are required for machine learning training [7; 8]. Statistical filling is more effective for data series with less dimensions that can establish a maximum model, provided that the relationship between missing eigenvalues and existing eigenvalues can be established through observation. The main methods of machine learning include missing value filling algorithm based on the nearest neighbor method, cyclic neural network, and matrix decomposition. Matrix decomposition can effectively explore the correlation between different time series for different dimensions of long time series problems. The matrix decomposition method is used to learn the overall characteristics of the time series matrix, which can be used to approximate the matrix with time characteristics in low-rank, and then complete the missing data [9]. Large-scale time series data are always accompanied by the missing problem. Therefore, the tensor completion method has been introduced into this field to complement the traditional data completion method based on probability and statistics [10; 11; 12]. The data completion scheme based on simple quantitative statistics has a relatively simple and efficient processing effect for small datasets and simple regression models, but it is not feasible for massive data in the era of data explosion. Modern research not only has many kinds of data variables and long time series, but also requires fast processing speed, high universality and portability. Multiple variables and long time series can better describe the complex causal relationship between each other [13; 14].