Abstract: The cesium atomic clock is a crucial component of modern atomic precision timekeeping. In recent years, the domestic optically-pumped cesium atomic clock, TA1000, has been widely utilized in time-sensitive applications. The clock difference prediction algorithm for atomic clocks significantly influences the performance of timekeeping systems. Different types of atomic clocks exhibit varying noise characteristics, which can impact the stability and accuracy of clock difference predictions. To identify a suitable clock difference prediction algorithm for the TA1000, a comparative analysis of three classical algorithms for clock prediction used in caesium clock was conducted: the first-order linear regression model, the autoregressive integrated moving average (ARIMA) model, and the Kalman filter model. By utilizing varying amounts of data for modeling, the study predicts clock differences over the next 12 h, 1 d, 2 d, and 5 d. The effectiveness of the clock difference predictions from the three models is analyzed and compared, highlighting the advantages and disadvantages of each model when applied to the TA1000. Experimental results suggest that the ARIMA model is favored for clock differences prediction of TA1000.