Based on support vector regression (SVR) and controlled autoregressive (CAR), we proposed a new non-linear multidimensional time series method named SVR-CAR that can show the dynamic characteristics of sample set as well as the effect of environmental factors. To evaluate the performance of SVR-CAR, we compared its predictions with those of four other commonly-used methods, using two sets of real-world data and one-step prediction. The results showed that SVR-CAR had the highest accuracy in prediction among the five methods, and had the advantages of structural risk minimization, non-linear characteristics, avoiding over-fit, and strong capacity for generalization. SVR-CAR has the potential to be widely used for predictions involving multidimensional time series data in ecology, agricultural sciences and economics.