一种确定滑动窗口规模的边界距离算法

摘要/Abstract

摘要： 由于大多装备的原始测量数据采集信息量大、密度高,现有的时间序列滑动窗口的降维方法采用经验值确定窗口大小,无法最大限度地保留数据的重要信息点,并且计算复杂度高。为此,文中研究了实际应用中滑动窗口对时间序列相似性技术的影响,提出了一种确定滑动窗口初始规模的算法。该算法构建拟合度更高的上下边界曲线,将趋势加权引入LB_Hust距离计算方法中,从而降低了数学建模难度,提高了装备数据相似性聚类与状态评估的效率。

关键词: LB_Hust距离, 滑动窗口, 数据挖掘

Abstract: Due to a large amount of information and high density of the original measurement data collected by most equipment,the existing time series sliding window dimension reduction method uses the empirical value to determine the window size,which cannot retain important information points of the data to the utmost extent,and has high computational complexity.To this end,the influence of sliding window on time series similarity technology in practical applications was discussed,and an algorithm for determining the initial scale of sliding window was proposed.The upper and lower boundary curves with higher fitting degree are constructed,and the trend weighting is introduced into the LB_Hust distance calculation method,which reduces the difficulty of mathematical modeling and improves efficiency of equipment data similarity classification and state evaluation.

Key words: Data mining, LB_Hust distance, Sliding window

中图分类号:

TP301

彭成, 贺婧, 池昊. 一种确定滑动窗口规模的边界距离算法[J]. 计算机科学, 2019, 46(6A): 482-487. https://doi.org/

PENG Cheng, HE Jing, CHI Hao. Boundary Distance Algorithm for Determining Sliding Window Size[J]. Computer Science, 2019, 46(6A): 482-487. https://doi.org/

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