共查询到18条相似文献,搜索用时 297 毫秒
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研究基于时间序列相似搜索技术的煤矿瓦斯涌出分析新途径,提出基于PPR的煤矿瓦斯监测数据相似搜索方法。实验采用玉华煤矿的真实煤矿瓦斯监测数据,评价指标为信息损失量及相似查询效率。与基于离散傅立叶变换(DFT)和离散小波变换(DWT)的时间序列相似搜索算法的对比实验显示:在相同压缩比下,3种方法的信息损失相近;但是基于PPR的相似搜索算法的平均查询效率分别比基于DFT和基于DWT方法高32%和34%。因此PPR算法适合用于瓦斯监测数据相似搜索。 相似文献
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为提高多元时间序列相似查询执行效率,采用了基于距离索引结构的相似查询算法。利用主成分分析方法对多元时间序列数据降维并在此基础上进行聚类,以聚类质心为参考点,将各类变换到一维空间,利用B+-tree结构进行索引查询,找到与查询序列最相似的k个MTS序列。实验表明查询效率和准确性都有比较大的提高。 相似文献
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基于小波变换的时间序列相似模式匹配 总被引:21,自引:1,他引:21
提出了一种新的时序相似模式匹配方法,它采用小波分析的方法实现时间序列数据的降维,采用小波序列表示原序列,将小波序列组织为多维索引结构R-tree存储,在该索引结构基础上,基于一种表示相似性的距离函数,定义了范围查询和最近邻查询算法,实验结果证明这种方法性能优于传统的基于傅立叶变换的相似模式匹配方法。 相似文献
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提出了一种基于多项式变换的二维整型离散余弦变换(DCT)快速算法,利用多项式变换将二维DCT变换的计算转化为一系列一维DCT变换及其变换系数的求和运算,减少了乘法和加法的计算量;利用提升矩阵,实现了整型DCT变换,进一步提高了运算效率的同时,使信号可精确重构。 相似文献
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互联网上每天都会产生大量的带地理位置标签和时间标签的信息,比如微博、新闻、团购等等,如何在众多的信息中找到在时间和空间地理位置上都满足用户查询需求的信息十分重要.针对这一需求,提出了一种对地理位置和时间信息的k近邻查询(ST-kNN查询)处理方法.首先,利用时空相似度对数据对象的地理位置变量和时间变量进行映射变换,将数据对象映射到新的三维空间中,用三维空间中两点之间的距离相似度来近似代替两个对象之间实际的时空相似度;然后,针对这个三维空间设计了一种ST-Rtree(spatial temporal rtree)索引,该索引综合了空间因素和时间因素,保证在查询时每个对象至多遍历1次;最后,在该索引的基础上提出了一种精确的k近邻查询算法,并通过一次计算确定查询结果范围,从而找到前k个结果,保证了查询的高效性.基于大量数据集的实验,证明了该查询处理方法的高效性. 相似文献
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该文提出了基于傅立叶变换的一种新的时间序列相似搜索算法。该算法利用高效的索引方法,达到快速的匹配,解决了多序列的子序列匹配问题。大量算例验证了该算法的通用性和有效性,它可以应用到求解各种时间序列相关的实际问题。 相似文献
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Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases 总被引:36,自引:3,他引:33
Eamonn Keogh Kaushik Chakrabarti Michael Pazzani Sharad Mehrotra 《Knowledge and Information Systems》2001,3(3):263-286
The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial
problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality
reduction on the data, and then indexing the reduced data with a spatial access method. Three major dimensionality reduction
techniques have been proposed: Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and more recently
the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Piecewise
Aggregate Approximation (PAA). We theoretically and empirically compare it to the other techniques and demonstrate its superiority.
In addition to being competitive with or faster than the other methods, our approach has numerous other advantages. It is
simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and
the index can be built in linear time.
Received 16 May 2000 / Revised 18 December 2000 / Accepted in revised form 2 January 2001 相似文献
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针对时间序列数据,提出一种新的基于动态时间弯曲的下界技术,该技术首先基于分段聚集近似的线性表示对原始序列进行降维,同时生成查询序列的网格最小边界矩形近似表示,然后利用基于动态时间弯曲距离对两者下界距离度量。实验结果表明,该下界技术与以往相关技术相比,能够产生更大的下界距离,具有更强的紧凑度、裁剪搜索空间能力以及更短的运行时间,有利于时间序列数据挖掘。 相似文献
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Efficient Similarity Search over Future Stream Time Series 总被引:2,自引:0,他引:2
With the advance of hardware and communication technologies, stream time series is gaining ever-increasing attention due to its importance in many applications such as financial data processing, network monitoring, Web click-stream analysis, sensor data mining, and anomaly detection. For all of these applications, an efficient and effective similarity search over stream data is essential. Because of the unique characteristics of the stream, for example, data are frequently updated and real-time response is required, the previous approaches proposed for searching through archived data may not work in the stream scenarios. Especially, in the cases where data often arrive periodically for various reasons (for example, the communication congestion or batch processing), queries on such incomplete time series or even future time series may result in inaccuracy using traditional approaches. Therefore, in this paper, we propose three approaches, polynomial, Discrete Fourier Transform (DFT), and probabilistic, to predict the unknown values that have not arrived at the system and answer similarity queries based on the predicted data. We also apply efficient indexes, that is, a multidimensional hash index and a B+-tree, to facilitate the prediction and similarity search on future time series, respectively. Extensive experiments demonstrate the efficiency and effectiveness of our methods for prediction and answering queries. 相似文献
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基于函数的时间序列分段线性表示方法 总被引:1,自引:0,他引:1
考虑到时间序列的时间特性对不同区段的影响以及时间序列数据动态增长的实际情况,在RPAA ( Reversed Piecewise Aggregate Approximation)和PAA(Piecewise Aggregate Approximation)方法的基础上,提出了一种新的时间序列分段线性表示方法FPAA(Founction Piecewise Aggregate Approximation)。FPAA方法通过定义函数影响因子,克服了RPAA和PAA方法的不足。该方法具有线性时间复杂度,满足下界定理,并且支持时间序列的在线划分。实验表明,与PAA方法和RPAA方法相比,所提出的方法可以较有效地进行时间序列的在线查询。 相似文献
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We introduce a new representation for time series, the Multiresolution Vector Quantized (MVQ) approximation, along with a distance function. Similar to Discrete Wavelet Transform, MVQ keeps both local and global information about the data. However, instead of keeping low-level time series values, it maintains high-level feature information (key subsequences), facilitating the introduction of more meaningful similarity measures. The method is fast and scales linearly with the database size and dimensionality. Contrary to previous methods, the vast majority of which use the Euclidean distance, MVQ uses a multiresolution/hierarchical distance function. In our experiments, the proposed technique consistently outperforms the other major methods. 相似文献
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滑动聚集平均近似PAA(Piecewise Aggregate Approximation)是一种表示时间序列的方法,它通过时间序列上滑动一个等宽的滑动窗口将时间序列分成小的区段。考虑到时间序列的时间特性q-不同区段的影响,本文提出了一种改进表示RPAA(Reversed Piecewise Aggregate Approximation)。RPAA表示对处于不同时间段的序列赋以不同的影响因子,具有线性时间复杂度,并且证明了RPAA满足下界定理,因而能够进行实际的查询。最后的实验表明该表示是有效的。 相似文献