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小波变换在信号处理、图像处理、模式识别、计算机视觉等 方面广泛应用。本文提出了一种基于Haar小波的时问序列相似 模式匹配模型．它首先对时间序列数据进行Haar小波变换．以 降低数据的维度。首先将所要查询的时间序列进行降维，也就是 对时间序列数据进行小波变换，对序列进行标准化131，得到降维 之后的小波序列．这样可以得到系数子集的良好近似采用尺度 序列表示原始序列．并将其看成多维空间中的一个点，通过计算 两序列差的平方和的平方根作为这两个时间序列的距离函效 (即欧氏距离)。用小波变换保持了局部性质。而欧氏距离就是要 判断相似性的距离函数．所以当在作完变换之后。通过低频分量 来压缩数据，成功的计算出实际距离的下界。如果计算的结果小 于一个由用户所定义的门槛值．则认为这两个时问序列是相似 的。 在-Of wavelet transform in signal processing, image processing, pattern recognition, computer vision, etc. Aspects of wider application. In this paper, Haar wavelet-based time series similarity Pattern matching model. It is first time-series data, Haar wavelet transform. To To reduce the data dimension. Will first want to check the time series dimension reduction, that is, Pairs of time series data using wavelet transform, standardize the sequence 131, to be reduced- After the wavelet sequence. This can be a good approximation coefficients of a subset of the use of scale Seque

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