This paper proposes a technique of piecewise aggregate approximation based on cloud model to resolve the high
dimensionality of time series. The entropy of cloud model is used to evaluate the stability of data points in a subsequence and
choose the subsequence with lower stability to further divide so that a series of cloud models can be obtained to approximate
time series. The similarity between two cloud model series is calculated. The proposed method can reduce the dimensionality,
and also can adaptively recognize and represent the essential features of time series. The results of experiments indicate that
the proposed method can guarantee the accuracy of similarity and improve the efficiency of time series data mining under
larger compress ratio.