数字趋势序列及其全序列匹配算法研究

针对基于点距离的时序数据分析和传统趋势序列分析的缺点,提出了数字趋势序列、序列的Lp距离、序列分段向量等概念,证明了包括“序列分段均值定理”在内的3个重要定理,设计了专门用于数字趋势序列的“基于序列分段向量（SSV）的全序列匹配算法”。算法使用片段斜率所对应的弧度值来度量片段的趋势,同时用趋势的保持时间来对趋势值进行加权,实现了数字趋势序列之间快速的全序列相似性搜索。     

基于短序列分组和拼接策略的子序列快速查询算法

子序列查询技术在金融、商业、医疗等领域均有重要应用,但因DTW（dynamic time warping）等相似性比对算法的时间复杂度较高,子序列长度对检索时间影响很大,限制了数据集上长子序列检索的效率。针对这一问题提出一种子序列快速查询算法。首先对数据集中特定长度下所有子序列进行分组并标记出代表性子序列;然后在查询时将查询序列切分成定长的小段序列,并用DTW算法确定与小段序列相似的代表子序列候选集;最后对候选集进行序列拼接,获取到查询结果序列。实验表明新算法效率较典型算法提高约10倍。     

基于Hadoop的异常传感数据时间序列检测

无线传感器网络中,异常时间序列的研究具有十分重要的意义。针对传统研究在海量数据环境中时间效率低下的问题,提出了基于Hadoop的异常时间序列检测算法。首先对时间序列进行预处理,然后在Hadoop的MapReduce操作中调用动态时间弯曲距离计算算法,实现了DTW距离计算的并行化,从而大大提高检测速度。同时针对传统DTW算法计算复杂度瓶颈问题以及传统约束方法准确率较低问题,提出了基于显著特征匹配的局部约束算法,对弯曲路径进行局部限制,在确保准确性的同时进一步降低了时间、空间复杂度。Hadoop平台下实验结果表明,该方法既提高了检测速度,又保证了检测准确率。     

基于时间序列优化和DTW的在线手写签名鉴别

针对传统DTW算法的不足,本文通过优化时间序列,提出了基于时间序列优化的在线手写签名鉴别算法。该算法弥补了传统DTW算法忽略签名时间序列长度的不足,并提高了签名认证准确率。实验结果说明算法是可行的。     

基于导数序列的时间序列同构关系发现

时间序列子序列匹配作为时间序列检索、聚类、分类、异常监测等挖掘任务的基础被广泛研究。但传统的时间序列子序列匹配都是对精确相同或近似相同的模式进行匹配,为此定义了一种全新的具有相似发展趋势的序列模式——时间序列同构关系,经过数学推导给出了时间序列同构关系判定的法则,并基于此提出了同构关系时间序列片段发现的算法。该算法首先对原始时间序列进行预处理,然后分段拟合后对各时间序列分段进行同构关系判定。针对现实背景数据难以满足理论约束的问题,通过定义一个同构关系容忍度参数使实际时间序列数据的同构关系挖掘成为可能。实验结果表明,该算法能有效挖掘出满足同构关系的时间序列片段。     

基于序列极值点分段的空中签名身份认证

空中签名序列长,为了解决传统的全局匹配方法造成的匹配慢、签名的局部信息丢失的问题,提出了对签名数据进行极值点分段再进行距离度量的方法。并针对传统DTW算法在极值点匹配中产生的不同极性极值点错匹配问题,提出了一种基于极值点匹配的改进DTW算法,约束DTW算法的匹配路径规则,避免错误匹配情况。在本地数据库上,系统的误拒率FRR和误纳率FAR分别达到了4.15%和3.82%。实验结果表明,与传统的全局匹配算法相比,先分段再进行相似度度量的方法使系统的认证精度和效率得到了提高。     

一种基于图论的计算蛋白质数据库代表序列的算法

许多生物序列数据库中都含有大量的冗余序列,这些冗余序列通常不利于对数据库的统计分析和处理,而且它们要占用更多的计算机存储和处理资源.针对这个问题,本文中我们设计了一种去除蛋白质冗余序列的算法.该算法基于图论最大独立集的概念来生成非冗余序列集合,对目前存在的不少蛋白质去冗余程序所采用的由Hobohm和Sander最早设计的一种首先将序列分成若干簇然后取出代表序列的算法进行了改进,使得生成了更多的非冗余代表序列集合,避免了一些非冗余的序列也被去除.我们开发出了实现该算法的程序FastCluster,可以用来去除蛋白质数据库中的冗余序列.     

基于ACV约束的序列模式挖掘算法

针对变值数据环境下的序列模式挖掘问题进行研究,提出一种针对变值数据的约束(ACV约束),用于表达用户在变值数据环境下对序列模式聚集特征的要求。在此基础上,提出一种基于ACV约束的序列模式挖掘算法,利用ACV约束的性质有效削减搜索空间。在用IBM数据生成器产生的序列数据以及真实数据上的实验结果表明,该算法能够有效利用ACV约束对无用的候选序列模式进行剪枝,减少冗余的搜索空间并提高挖掘效率。     

基于动态规划求解时间序列DTW中心

中心时间序列表明了一个时间序列集合中的公共特征,是时间序列聚类的重要手段。提出了一个利用动态规划求解两条时间序列DTW中心的方法,即以最小化中心序列到两条样本序列的DTW距离平方和为目标,递归求解最优解。在此基础上,给出了基于中心与样本匹配度的剪枝方法,降低了时间复杂度。并在理论上证明了该方法可以获得最优解。实验结果显示,相比于DBA算法,该算法能够获得更小的DTW距离平方和,并且有更好的鲁棒性。     

基于Hadoop的水文时间序列相似性研究与应用

传统DTW算法复杂度高,特别当处理海量数据时,耗时长.为了从算法和实现手段两方面同时入手,提高DTW运算效率,提出基于Hadoop平台,以FastDTW方法实现的水文时间序列相似性查找方法.首先利用小波变换对数据去噪,接着对水文时间序列进行语义化,然后在Hadoop的MapReduce过程中调用FastDTW方法实现DTW距离的云计算,得出与查询序列最相似的匹配序列.通过实验与串行查找进行对比,验证该方法用时短,匹配效果好,能够满足实际应用需求.     

Robust gesture recognition using feature pre-processing and weighted dynamic time warping

Gesture recognition is a technology often used in human-computer interaction applications. Dynamic time warping (DTW) is one of the techniques used in gesture recognition to find an optimal alignment between two sequences. Oftentimes a pre-processing of sequences is required to remove variations due to different camera or body orientations or due to different skeleton sizes between the reference gesture sequences and the test gesture sequences. We discuss a set of pre-processing methods to make the gesture recognition mechanism robust to these variations. DTW computes a dissimilarity measure by time-warping the sequences on a per sample basis by using the distance between the current reference and test sequences. However, all body joints involved in a gesture are not equally important in computing the distance between two sequence samples. We propose a weighted DTW method that weights joints by optimizing a discriminant ratio. Finally, we demonstrate the performance of our pre-processing and the weighted DTW method and compare our results with the conventional DTW and state-of-the-art.     

一种支持不同时间尺度的视频相似性匹配算法*

针对不同时间尺度视频间的匹配问题,如人为再编辑（快进、慢放等）视频与原始视频间的匹配以及不同帧率视频间的检索等,提出了一种基于动态时间规划的最优匹配算法。在子片段的基础上进行视频相似性匹配,通过极小化两段视频的整体距离建立视频之间的子片段对应关系,引入搜索门限值,将全局搜索转变为在门限区域内的局部搜索,保持视频内部各子片段之间的时序关系并能处理非线性偏移。该算法能够成功地匹配不同时间尺度的相似视频,并能实现海量视频的快速检索。实验结果证明了该算法比传统的基于视觉相似性的视频片段检索算法更快速有效。     

弹性粗粒度动态弯曲时序相似性算法

针对动态时间弯曲(DTW)算法在提高计算速度同时不能兼顾分类正确率的问题,提出了一种基于朴素粒计算思想的弹性粗粒度动态时间弯曲(CG-DTW)算法。首先,通过计算时序方差特征的方法来获取较优的时序粒度,用粒度特征代替原始序列;其次,再代入执行DTW算法,允许动态调整被比较时序粒间的弹性大小,从而获得相对最优的时序对应粒;最后,在对应最优粒的情况下计算DTW距离。同时引入下界函数的提前终止策略进一步提高CG-DTW算法效率。实验结果表明,所提算法要比经典算法运行速率提高21.4%左右,比降维策略算法正确率提高近32.3个百分点,尤其是长序列的分类,CG-DTW能够在保持正确率的情况下兼顾较高的运行效率。CG-DTW在实际应用中能适应不确定长序列分类。     

Exact indexing for massive time series databases under time warping distance

Among many existing distance measures for time series data, Dynamic Time Warping (DTW) distance has been recognized as one of the most accurate and suitable distance measures due to its flexibility in sequence alignment. However, DTW distance calculation is computationally intensive. Especially in very large time series databases, sequential scan through the entire database is definitely impractical, even with random access that exploits some index structures since high dimensionality of time series data incurs extremely high I/O cost. More specifically, a sequential structure consumes high CPU but low I/O costs, while an index structure requires low CPU but high I/O costs. In this work, we therefore propose a novel indexed sequential structure called TWIST (Time Warping in Indexed Sequential sTructure) which benefits from both sequential access and index structure. When a query sequence is issued, TWIST calculates lower bounding distances between a group of candidate sequences and the query sequence, and then identifies the data access order in advance, hence reducing a great number of both sequential and random accesses. Impressively, our indexed sequential structure achieves significant speedup in a querying process. In addition, our method shows superiority over existing rival methods in terms of query processing time, number of page accesses, and storage requirement with no false dismissal guaranteed.     

A fast projection algorithm for sequence data searching

In real life, data often appear in the form of sequences and this form of data is called sequence data. In this paper, a new definition on sequence similarity and a novel algorithm, Projection Algorithm, for sequence data searching are proposed. This algorithm is not required to access every datum in a sequence database. However, it guarantees that no qualified subsequence is falsely rejected. Moreover, the projection algorithm can be extended to match subsequences with different scales. With careful selection of parameters, most of the similar subsequences with different scales can be retrieved. We also show by experiments that the proposed algorithm can outperform the traditional sequential searching algorithm up to 96 times in terms of speed up.     

Sequential data classification by dynamic state warping

The ubiquity of sequences in many domains enhances significant recent interest in sequence learning, for which a basic problem is how to measure the distance between sequences. Dynamic time warping (DTW) aligns two sequences by nonlinear local warping and returns a distance value. DTW shows superior ability in many applications, e.g. video, image, etc. However, in DTW, two points are paired essentially based on point-to-point comparisons without considering the autocorrelation of sequences. Thus, points with different semantic meanings, e.g. peaks and valleys, may be matched providing their coordinate values are similar. As a result, DTW may be sensitive to noise and poorly interpretable. This paper proposes an improved alignment method, dynamic state warping (DSW). DSW integrates the dynamic information of sequences into DTW by converting each time point into a latent state. Alignment is performed by using the state sequences. Thus, DSW is able to yield alignment that is semantically more interpretable than that of DTW. Using one nearest neighbour classifier, DSW shows significant improvement on classification accuracy in comparison with Euclidean distance (68/85 wins), DTW (70/85 wins) and its variants. We also empirically demonstrate that DSW is more robust and scales better to long sequences than Euclidean distance and DTW.     

一种基于DTW聚类的水文时间序列相似性挖掘方法

水文时间序列相似性挖掘是水文时间序列挖掘的重要方面,对洪水预报、防洪调度等具有重要意义。针对水文数据的特点,提出了一种基于DTW聚类的水文时间序列相似性挖掘方法。该方法先对数据进行小波去噪、特征点分段以及语义划分,再基于DTW距离对划分后的子序列做层次聚类并符号化;然后根据符号序列间的编辑距离筛选候选集;最后通过序列间的DTW距离进行精确匹配,获取相似水文时间序列。以滁河六合站的日水位数据进行实验,结果表明,所提方法能够有效地缩小候选集,提高查找语义相似的水文时间序列的效率。