## 1 Introduction
Now, neural network technology is often used to deal with the above situations and delete the missing data in order to form a complex intelligent model, but it is not the best solution for missing data because deletion may strengthen or weaken the connection degree of a causal relationship. Based on this, scholars have explored the application of tensor decomposition technology to data completion in multivariate long time series, trying to improve the resolution speed and data missing problems. Recent studies have found that low-rank matrices have certain advantages in the analysis of multivariate long time series data [11; 12], including the sequence tensor completion method [15]. The sequence tensor complement restores the potential tensor from the sampling structure of the time series, allocates the position of the missing items as needed, seamlessly integrates the future value of the time series into the framework of the missing data and improves the data completion accuracy [15]. The low-rank matrix completion method performs singular spectrum analysis and singular value decomposition on the time series in order to complete the low-rank completion of the missing data of the time series, although its calculation is large [16]. Therefore, by adding a time dimension to transform the status time series into a high-dimensional tensor, the cost of computing complexity is better solved. This is also in line with the law of human activities, both short and long term activities, so there are studies using tensors (sensors \(\times\) 1 day \(\times\) 24 h) which indicate the above activity mode [17; 18]. The dependency between sensors is preserved, providing a new feasible scheme for capturing local and global time patterns [16]. More scholars have combined the autoregressive moving average model with the tensor model to propose the low-rank matrix autoregressive tensor completion model and have achieved good results in the completion and prediction of financial time series data [19]. The mean-based low-rank autoregressive tensor completion mainly includes completing low-rank matrix decomposition/tensor completion and constructing time series autoregressive models, as well as processing the missing data with the neighboring data mean instead of zero before the operation. The low-rank matrix completion model uses the underlying low-rank structure to recover incomplete matrices (assuming that the long-term landslide data sequence is incomplete) [20]. Considering that the deformation displacement of landslide has a great correlation with the previous deformation, the autoregressive model is constructed to represent the deformation law of landslide displacement with time. The autoregressive regularizer is introduced in the low-rank matrix decomposition to characterize the temporal dynamics in landslide displacement deformation, and the learned autoregressive regularizer is implemented to predict the temporal factor matrix, thus realizing the landslide displacement monitoring data completion and predictive modeling [12]. The purpose of this study is to establish a new method for completing and predicting landslide displacement data based on MLATC. In this paper, the causes of data loss of landslide displacement are analyzed. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as an example, the data completion and prediction algorithm are designed by using MLATC. Then, the landslide displacement data are divided into training set and test set, and the random missing and non-random missing are selected for corresponding data completion and prediction. The designed model can achieve an accurate completion and prediction of landslide displacement. Finally, a comparative analysis with existing models verifies the effectiveness of the model.