Abstract: Objective To explore the effect of auto-regressive integrated moving average model-X(ARIMAX) model on predicting influenza trend in Urumqi, and to provide basis for further application of modeling in forecasting infectious diseases. Methods Data on the number of influenza-like illness(ILI) cases and atmospheric particulate matter(PM_2.5 and PM₁₀) concentrations were collected from January 2013 to December 2016 in Urumqi. We used R to construct auto-regressive integrated moving average (ARIMA) model and predicted the number of ILI cases in the first 10 weeks in 2017 using ARIMAX model. Results From January 2013 to December 2016, the total number of ILI cases was 161 773 and the number of average weekly ILI cases was 777. The time series of ILI cases indicated that there was a high prevalence during winter and spring. ARIMA (1, 0, 0) model was established based on weekly ILI cases and akaike information criterion (AIC)=2 549.03. PM_2.5 and PM₁₀ were considered as parameters to build ARIMAX model (AIC=2 535.51) and they were shown to be statistically significant. Using iterative method to predict the data of the first ten periods (i.e. 10 weeks), the results indicated only 3 period (i.e. 3 weeks) prediction had the minimal error.The mean absolute percentage error (MAPE) of the two models was 12.019 74% and 12.014 17% respectively, suggesting both of them had a good predictive accuracy. Conclusions ARIMA model and ARIMAX model can predict trend of ILI cases incidence in short time and provide evidence for influenza monitoring and prevention.