## 1 Introduction  
Landslides are one of the most serious geological disasters, which frequently occur worldwide and seriously affect people's property and environmental safety. Therefore, slope deformation monitoring and early warning are of great significance [1, 2]. Slope deformation prediction is one of the key factors in the early warning of slope failure. Establishing an accurate prediction model for forecasting slope deformation is critical. Slope deformation prediction models are mainly divided into physics-driven models and data-driven models [3]. Data-driven models, which are widely used and possess higher prediction accuracy, fully consider the complexity and nonlinearity of the deformation characteristics of slopes. [4, 5]. The time-series forecasting method [6, 7] is a typical data-driven approach for slope deformation, which predicts the future by learning the potential laws in the historical time-series displacement data of slopes. The time-series displacement data of slopes, which can directly reflect the deformation characteristics of slopes, are a series of displacement observation values obtained by the slope field monitoring equipment at a certain time interval. In practical engineering applications, field monitoring devices of the slopes are expensive, and the monitoring process is influenced by many factors [8; 9]. Data analysis approaches such as statistical methods and machine learning approaches have been widely applied to various engineering problems with outstanding achievements in recent years. Therefore, exploiting the existing time-series forecasting approaches to establish a reasonable and accurate prediction model based on the monitored displacement data is a common solution to slope deformation prediction. Currently, time-series forecasting methods mainly include three types: statistical approaches, traditional machine learning approaches, and deep learning approaches. The most popular statistical model is the autoregressive integrated moving average (ARIMA). The ARIMA model is mainly used to predict linear time-series data with accurate forecasting performance on short-term time series [10; 11]. However, although the ARIMA can be adopted to achieve a highly accurate linear prediction, it cannot accurately forecast nonlinear time series. The ARIMA model needs to be combined with machine learning methods such as neural networks to improve the prediction performance on nonlinear time series [12; 13]. For example, [PERSON] et al. [14] pointed out that prediction models combining machine learning approaches and ARIMA outperformed a single ARIMA model. The slope system is a complex nonlinear dynamic system, and the stability and deformation of slopes are affected by many uncertain factors, such as rock and soil properties, geomorphological features, rainfall, human engineering activities, and temperature changes. The interaction of these features leads to the complexity, ambiguity and randomness of slope deformation. Therefore, simple statistical methods are not suitable for complex nonlinear time-series problems of slope deformation, while machine learning is a promising alternative to traditional statistical methods due to its nonlinear learning capabilities. The most applied traditional machine learning approaches for time-series forecasting are support vector regression (SVR) [15; 16] and extreme gradient boosting (XGBoost). Compared with statistical approaches, machine learning approaches more easily capture the complexity, dynamics, and nonlinear characteristics of the nonlinear time-series displacement data of slopes. Currently, machine learning approaches have made some achievements in slope displacement prediction research [17; 18]. For example, [PERSON] et al. [19] proposed a coupled model of GA-SVR based on time series and a genetic algorithm and achieved an accurate landslide displacement prediction. In recent years, deep learning has developed rapidly and performed better than traditional machine learning models in many studies [20].