Abstract: The model of wavelet least squares support vector machines(WLSSVM) is optimized by adaptive particle swarm optimization(APSO),and a new model named as wavelet least squares support vector machine based on adaptive particle swarm optimization(APSO-WLSSVM) is put forward.The model combines good time domain,frequency domain resolving ability of wavelet transformation and nonlinear learning ability of SVM.The adaptive particle swarm optimization is used to optimize the parameters of SVM so as to avoid artificial arbitrariness and enhance the forecast accuracy.For comparison,the model of APSO-WLSSVM and the traditional SVM(Gauss kernel function) are used to forecast the same displacement time series.The result shows that the former is better than the latter in forecast accuracy.The model is used to forecast the left back slope and diversion tunnel of Jinping First-stage Hydropower Station.The forecast values are in good agreement with the measured ones,indicating that the APSO-WLSSVM is feasible and precise and can be well applied to the forecast of displacement time series.