Abstract
This work explores the characteristics of the rental behavior of Carsharing users on the basis of actual operation data from a car-sharing company in Beijing, China. Considering the random fluctuation of carsharing rental, the original data are clustered by fisher ordered clustering algorithm, and one day is divided into five time periods. Then we construct the SARIMA model for all data and the combination model of five SARIMA in different time periods. Finally, the evaluation indicators are calculated to compare the two modeling effects. The result is that establishing SARIMA prediction model after the use of fisher ordered clustering algorithm to divide the time period is better and the prediction accuracy is higher.
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