Landslide disasters frequently occur along the banks of reservoirs in China, and it is of great significance to use high-precision optimization algorithms to predict the displacement time series of slopes for disaster prevention and reduction. The displacement time series of slopes usually exhibit highly nonlinear characteristics, and traditional models have difficulty accurately predicting them. Therefore, this paper proposes a slope displacement time series prediction model based on optimized empirical mode decomposition (EMD) and least squares support vector machine (LSSVM). The model adopts EMD based on the soft sifting stop criterion (SSSC-EMD), which can adaptively decompose the displacement time series of slopes into multiple intrinsic mode components and one residual component. The residual component is defined as a trend term; the components are clustered using the K-means clustering method and defined as periodic and random terms. The trend term is predicted using the least squares method; the periodic and random terms are predicted using an LSSVM regression model. The predicted values are accumulated and summed to obtain the cumulative displacement prediction value. Taking the Shankouyan dam as an example, the SSSC-EMD-LSSVM model is used to predict the displacement time series of the slope at the site. The results show that the model can effectively predict the displacement time series with higher accuracy than traditional BP neural networks and LSSVM.