An Piecewise Linear Fitting Algorithm for Infinite Time Series
An Piecewise Linear Fitting Algorithm for Infinite Time Series
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发表时间：
2010
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通讯作者：
Xia Shi-xiong
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作者：
Xia Shi-xiong
为了解决传统PLF算法依赖于时间序列长度和领域知识的问题，提出了一种无限时间序列分段线性拟合算法（ITS-PLF），该算法考虑了关键点保持时间的统计属性，确定关键点的选择区间，如果极值点的保持时间超过选择区间，则关键点的保持时间将超过选择区间。实验结果表明，ITS-PLF算法不依赖于时间序列的长度和领域知识，能够根据数据压缩比的变化，有效识别关键点，自适应拟合时间序列。
In order to resolving the problem of depending on the length of time series and domain knowledge of traditional PLF algorithm,we proposed a Piecewise Linear Fitting algorithm for Infinite Time Series(ITS-PLF).To determine the interval of Key Points selecting,the statistical attributes of maintaining time of these Key Points was considered.If the maintaining time of a Extreme Point beyond the selection interval,the relation between the threshold angle and the angle of three consecutive data points containing the Extreme Point was selected to determine whether the Extreme Point was a Key Point or not.The experimental results show that ITS-PLF algorithm does not depend on the length of time series and domain knowledge,can effectively identify the Key Point and adaptively fit the time series according to the changing of the data compression ratio.