Abstract: Positioning data analysis and post-processing is essential part in the application of Global Navigation Satellite System in S/M and geological hazard monitoring and forecast project. Generally, the smooth data obtained after the Kalman filter processes the positioning data can eliminate noise interference and obtain data close to the true value. However, in the case of a long-term span, the periodic interference is difficult to identify and filter out in a short time, which is reflected as a kind of lower frequency noise fluctuation. This paper assumes that the fluctuation interference is periodic, and uses the X-11 decomposition time series analysis method for data processing. After smoothing, the variance of the positioning data is reduced from 4.733 to 2.683, and the accuracy is increased by 43.3%. And perform autoregressive integrated moving average mode (ARIMA) modeling and prediction on the split data. When compare the restored data with the direct prediction data, we can draw the conclusion that the accuracy of the separate prediction and integration restoration is basically higher than that of the direct prediction by 5% to 10%, so as to deal with the problem of poor real-time smoothing.