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An efficient and accurate optimization method of sliding window size for PAA

机译:一种高效准确的PAA滑动窗口尺寸优化方法

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摘要

PAA is an important algorithm in time series dimensionality reduction. However, how to determine the sliding window keeps an open issue for PAA and its derivatives. In this paper, a new optimization method to decide the PAA window is proposed based on root mean square distance measure. A rate of information loss is proposed to overcome the scalability issue, which can be used to balance information loss and query performance improvement caused by PAA transformation. Experiment results with a real time series dataset demonstrate that the method is effective and feasible to determine the PAA window size and optimize the whole performance of PAA algorithm.
机译:PAA是减少时间序列维数的重要算法。然而,如何确定滑动窗口对于PAA及其衍生产品仍然是一个悬而未决的问题。提出了一种基于均方根距离度量的PAA窗口确定的优化方法。提出了一种信息丢失率来克服可伸缩性问题,该信息可用于平衡由PAA转换引起的信息丢失和查询性能的提高。实时序列数据集的实验结果表明,该方法是确定PAA窗口大小并优化PAA算法整体性能的有效方法。

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