To improve the measurement accuracy of a parasitic time grating sensor, the working principle and dynamic error composition of the sensor were analyzed deeply and the main error components including constant error, periodic error and random error were obtained. According to the error characteristics of parasitic time grating, a high precise prediction model for dynamic error of the parasitic time grating was established and the modeling method was compared with other modeling methods. The Bayesian prediction model interpolated with standard values was chosen to build the error prediction model based on the actually measured dynamic error data of first pole in the sensor. Then, a part of actual measurement error data were inserted in the specific location of subsequent pole to establish the error prediction model to predict the dynamic error of 83 poles of the sensor. The modeling method of cubic spline interpolation and BP neural network were used to build the whole circle dynamic error model of parasitic time grating sensor and compared with the above Bayesian model. The modeling verification experiment results show that the modeling time of cubic spline interpolation method is the shortest (0.62 s), but the modeling accuracy is not high(16.050 0″). The modeling time of Bayesian prediction model is slightly longer than that of the cubic spline interpolation(0.86s), but the modeling accuracy is the highest one(0.415 3″). The modeling time of BP neural network method is the longest one (32 min), and the modeling accuracy is the lowest one (19.680 2″). Moreover, the modeling data points of Bayesian prediction model interpolated with standard value(69395) is far less than that of cubic spline interpolation and BP neural network(235526). Therefore, Bayesian prediction model interpolated with standard values saves a lot of calibration time and modeling data points, and can be used for high precision modeling and dynamic measurement error correction of parasitic time grating sensors.