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小时间序列在宏观经济领域普遍存在,对小时间序列的分类预测也有着广泛的需求.由于小时间序列蕴含的信息不充分,有效地提高小时间序列分类预测的可靠性非常困难,目前也缺少这方面的研究.针对这种情况,在基于引入平滑参数的高斯核函数估计属性边缘密度的基础上,建立用于小时间序列分类预测的动态朴素贝叶斯分类器,并给出平滑参数的同步和异步优化方法.实验结果表明,优化能够显著提高小时间序列分类预测的准确性.
Small time series are widely existed in the macroeconomy field, and they also have a wide range of needs for the classification and prediction of small time series. Since the information contained in small time series is not enough, it is very difficult to effectively improve the reliability of small series time series prediction. In this paper, a dynamic naive Bayesian classifier for small-time series classification and prediction is established based on Gaussian kernel function introducing the edge density of smoothed parameters, and the smoothing parameters Synchronous and asynchronous optimization methods.The experimental results show that the optimization can significantly improve the accuracy of the classification and prediction of small time series.