In order to solve the accuracy problem of the prediction algorithm for the lowest airfare in the future, this paper presents an United Intelligent Forecasting algorithm (UIF)consisting of two sub-algorithms, the Composite Weighted Time Series method(CWTS) and the Similarity Time Average method (STA) based on the idea of time series. CWTS calculates the sub-price on the target day from the quoted prices on the same day and a period of time in the past. STA calculates the sub-price on the target day from the prices on the similar period of time. Experiments on the real datasets show that UIF outperforms the traditional prediction algorithm and provides enhanced accuracy for airfare prediction. This airfare forecasting model based on time series can effectively solve the predictive conflict between sequences with smooth and fluctuating trends and thus a class of predictive analysis problems for the lowest airfare of air tickets at all kinds of time points are solved.