受库水位涨落及降雨等影响，库区滑坡位移表现出明显的周期性。基于位移时间序列分析，将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势，其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性，其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例，考虑长江水位与降雨量影响，采用H-P滤波法从滑坡位移中分解出趋势项及周期项，利用差分自回归滑动平均模型（ARIMA）对趋势项进行平稳处理并计算趋势项预测值，利用向量自回归模型（VAR）计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较，表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性，位移预测效果较好。

Landslide displacement in Three Gorges Reservoir area is of periodicity due to water level change, rainfall and so on. Based on the time series analysis, landslide displacement can be divided into the trend displacement reflecting the long-term trend of landslide, which is the response of geologic structure; and the periodic displacement reflecting the volatility of landslide, which is mainly affected by external factors such as rainfall. Taking Taping landslide in Three Gorges Reservoir area for example and considering the influences of water level change and rainfall, the trend displacement and periodic displacement are evaluated by Hodrick-Prescott (H-P) filter forecasting method. Difference auto-regressive integrated moving average (DARIMA) model is utilized to smooth the curve of trend displacement, and then compute the predicted value of trend displacement. Vector auto-regressive (VAR) model is used to predict the periodic displacement. The overall predicted displacement is obtained by adding the predicted values of trend displacement and periodic displacement, which is compared with the monitoring displacement and one predicted by other forecasting methods. The results show that the predicted displacements by this proposed method are in better agreement with the monitoring data; the proposed comprehensive model can better reflect the trend and volatility of landslide displacement.