Objective：To explore the feasibility of two time series models for predicting tuberculosis epidemic in Shanghai，so as to provide a scientific reference for the prevention and control of tuberculosis in Shanghai. Methods：The epidemiological data of tuberculosis from Jan.2007 to Jun.2018 in Shanghai was collected. The monthly cases of tuberculosis from Jan.2007 to Dec.2017 was fitted by multiple seasonal ARIMA model and the exponential smoothing model. We predicted the monthly number of tuberculosis cases from Jan. to Jun. 2018 using the established models and comparcd the results to the real values. Results：ARIMA（0，1，1）×（1，1，2）12 was the optimal ARIMA model whose RMSE was 76.27 and the sum of the relative error from Jan. to Jun.2018 was 0.402；The optimal model constructed by exponential smoothing was Holt-Winters additive exponential smoothing model with RMSE of 69.61 and a sum of relative error of 0.292 from Jan. to Jun.2018. Conclusion：The exponential smoothing model could be fitted more effectively and had higher predictive accuracy. In short，it has important guiding significance for the prevcntion and control of tuberculosis in Shanghai.