中文摘要：

针对经验模态分解（EMD）的不足之处，对原有EMD方法中利用上下包络的乎均值得到平均包络进行了改进，采用三次样条对连续极值点的平均值进行插值获得乎均包络。通过这种方式，增加了近似极值点，在“筛”过程的每次循环中，只需要一次而不是两次祥务插值，缓解了“逆冲”和“欠冲”现象，改进了EMD方法，然后引用改进的EMD方法降低序列的维度，并用K均值算法实现模式匹配．实验结果表明，提出的在对EMD进行改进的基础上实现模式匹配的方法，优于传统的基于小波的模式匹配方法。

英文摘要：

Overshoot and undershoot problems will occur during the course of obtaining envelopes of time series with spline interpolation. If these problems can not be solved properly, redundant intrinsic mode functions （IMF） will be produced when a time series is decomposed by empirical mode decomposition （EMD）, and precision of EMD will become lower. To ameliorate EMD algorithm, an effective method was proposed, which used the means of successive extrema instead of the envelope mean to obtain the mean envelope. In this way, additional boundary and interior data points were created, and only one spline interpolation was required rather than two in each loop of the sifting process. Time complexity was reduced, overshoot and undershoot problems were alleviated, and EMD method was improved. Then dimensionality of time series with the improved EMD technique was reduced, and pattern matching was realized using K-means algorithm. At first, trend series were clustered, which were decomposed by EMD method. And then, accurate similar series patterns were reached by calculating distance of the clustered series in the category the trend of the query belongs to. Experimental results show performance of the new method, based on the improved EMD, is better than that of the wavelet-based pattern matching method.