Generally speaking, time-series data can be divided into three ingredients such as trend, random and season. Analyzing and modeling through the available time-series data, change law can find things contained. A kind of high precision data fitting method was designed for multiple periodic time-series data. Firstly, the algorithm eliminated random component of fitted time-series data. Secondly, several co-primed basic cycles were analyzed by the application of the theory of autocorrelation function. Finally, multiple periodic time-series data was fitting by using multiple sets of Fourier series based on the least squares principle. Practical application proved the effectiveness and progressiveness of the algorithm.