The small time series exists generally in the field of macroeconomy.There are wide demands for the classification of small time series in macroeconomy.Because the information contained in the small time series is not sufficient, it is very difficult to effectively improve the reliability of small time series classification.In view of this situation, on the basis of using the Gaussian kernel function of introducing the smoothing parameter to estimate the attribute marginal density, the dynamic naive Bayesian classifier for small time series classification is presented, and the synchronous and asynchronous optimization method for smoothing parameters are given.The experimental results show that the classification accuracy of the small time series classifier can be improved significantly by optimization.