Objective:To choose the optimal time series model for monthly gout incidence prediction. Methods:Monthly gout incidence data were collected retrospectively from an affiliated hospital of Chongqing Medical University from January 2011 to December 2014.And exponential smoothing method

ARIMA（p

d

q）

GM（1

1）and Markov model were adopted to fit the trend of monthly gout incidence

respectively. The efficiency comparison between different models was judged by mean absolute percentage error

and model with least mean absolute percentage error was used for future prediction. Results:Monthly gout incidence increased greatly

and cyclicality was observed after September 2012. But only sequence fluctuation was observed on the whole observation period. Results of trend prediction among four time series models were in consistent with each other

but their efficacy were different. The maximum and minimum mean absolute percentage error was in GM（1

1）model and ARIMA（1

1

0）model

respectively. Thus fitting results of ARIMA（1

1

0）model was mostly close to the actual situation. Conclusion:The ARIMA（1

1

0）model have the best imitative effect.Hence

ARIMA（1

1

0）model should be used for future monthly gout incidence prediction.