The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure. The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variation for prediction. In order to avoid prediction degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best A series of tests are performed to evaluate themodeling mechanism and prediction results that that Nu-SVR models can reflect the variation tendency of time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is also implemented to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.