1) Intrinsic Trend Subsequence
固有趋势子序列模式
2) Intrinsic Subsequence
固有子序列模式
3) trend series
趋势序列
1.
It decomposes at first a time series into a trend part and some detail parts using the EMD,and then divides all trend series into two sets: training sets and testing sets.
先利用经验模式分解实现时间序列趋势的提取,再把所有的趋势序列分成训练集和测试集2个部分。
2.
After clustering the transformed trend series, rough similar time series will be obtained.
首先将时间序列经EMD分解成细节部分和趋势部分,对低频趋势部分的序列数据进行线性分段近似表示,完成对序列数据的压缩,并将其变换成一种0-1串的形式,以适应趋势序列的快速匹配;然后通过对趋势序列模式聚类,达到对序列的粗匹配;最后对粗匹配的序列进行距离计算,从而获取细匹配的模式·实验结果表明该算法是有效的
4) trend sequence
趋势序列
1.
Subsequence matching algorithm between number trend sequences;
数字趋势序列的子序列匹配算法
2.
To overcome the shortcomings of concept indistinct and slow speed in time series similarity searching based on point distance, a piecewise trend sequence(PTS)and a variable step algorithm for sub-trend sequence searching based on PTS were proposed.
为了克服基于点距离的时间序列相似性搜索物理概念模糊和速度慢的缺点,提出时间序列的分段趋势序列(PTS)概念,并在此基础上提出一种变步长趋势子序列搜索算法。
3.
The paper proposes a segmentation method based on important points, by which time series data can betransformed into trend sequence.
提出了一种新的基于重要点的分段方法,将时间序列数据转换为趋势序列。
5) the time series model with deterministic trend
趋势时间序列模型
6) Number trend sequence
数字趋势序列
补充资料：固有
本来有的；不是外来的：～文化。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条