Wind power ramps cause large-amplitude power fluctuation which harmfully affects the stability of power system's operation. As a new issue in wind power integration, the existing ramp forecasting methods still has some imperfection, e.g., harmonization on long-term trend and short-term precision. Therefore, an advanced method is proposed in this paper, mainly focus on improving the performance of wind power ramp prediction. This method utilizes wind power curve to build a primary model which can capture the trend of wind power variation. Then, prediction residual of the primary model is corrected by a MSAR (Markov-Switching-Auto-Regression) model which combining the advantages of AR models and Markov chain. Finally, an improved swinging door algorithm is applied to extract linear segments, and ramp definitions are used to detect ramp events. Actual wind farm data is used to test the proposed method. Comparison with traditional methods are presented, the numerical results validate that the proposed approach has improved performance not only on wind power prediction but also on ramp prediction. (C) 2019 Elsevier Ltd. All rights reserved.