Use a method of time series piecewise linear representation based on feature points as a way for pattern representation.
使用一种基于特征点的时间序列线性分段方法作为时间序列的模式表示。
The optimization function is defined to be the sum of the squared error between the previous trace and the current pattern representation.
我们定义优化目标函数为前时刻轨迹和当前时刻特征提取结果的均方误差和，推导了相应的轨迹学习算法。
The pattern representation of time series itself has the function of compress data and keeps the basic shape of time series, and it has a certain extent effect of deleting noises.
时间序列的模式表示本身就具有压缩数据、保持时间序列基本形态的功能，并且具有一定的除噪能力。
The Non-symmetry and Anti-packing Model (NAM) is suitable for representations of image pattern, audio pattern, video pattern, and text pattern, and it is a general pattern representation model.
非对称逆布局模型（NAM）适用于图像模式、语音模式、文本模式、视频模式的表示，是一个通用型的模式表示模型。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
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