全局约束对时间序列数据库相似性度量的影响
The Influence of Global Constraints on Similarity Measures for Time-Series Databases
摘要：
时间序列由在时间上重复测量获得的一系列值或事件组成。时间序列分析是许多应用领域的重要工具，例如股票市场分析，过程和质量控制，自然现象观察，医疗等。在许多类型的时间序列分析中，一个重要组成部分是选择适当的距离/相似性度量。迄今为止，已经提出了许多措施，其中最成功的是基于动态规划的措施。然而，由于具有二次时间复杂度，在动态规划过程中，为了加快计算速度，经常使用全局约束来限制矩阵中的搜索空间。此外，据报道，这种约束措施也可以实现更好的准确性。本文研究了基于动态规划的两种典型时间序列距离/相似性度量方法：动态时间规整（DTW）和最长公共子序列（LCS），以及全局约束对它们的影响。通过大量的时间序列数据集上的大量实验，我们展示了如何全局约束可以显着减少DTW和LCS的计算时间。我们还表明，如果约束参数是足够紧（小于10-15%的时间序列长度），约束措施变得显着不同，从它的无约束对应，在这个意义上产生质的不同的1-最近邻图。这一观察结果解释了使用约束措施时精度增益的潜力，强调了需要仔细调整约束参数，以实现速度和精度之间的良好权衡。
英文摘要：
A time series consists of a series of values or events obtained over repeated measurements in time. Analysis of time series represents and important tool in many application areas, such as stock market analysis, process and quality control, observation of natural phenomena, medical treatments, etc. A vital component in many types of time-series analysis is the choice of an appropriate distance/similarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. Being of quadratic time complexity, however, global constraints are often employed to limit the search space in the matrix during the dynamic programming procedure, in order to speed up computation. Furthermore, it has been reported that such constrained measures can also achieve better accuracy. In this paper, we investigate two representative time-series distance/similarity measures based on dynamic programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), and the effects of global constraints on them. Through extensive experiments on a large number of time-series data sets, we demonstrate how global constrains can significantly reduce the computation time of DTW and LCS. We also show that, if the constraint parameter is tight enough (less than 10-15% of time-series length), the constrained measure becomes significantly different from its unconstrained counterpart, in the sense of producing qualitatively different 1-nearest neighbor graphs. This observation explains the potential for accuracy gains when using constrained measures, highlighting the need for careful tuning of constraint parameters in order to achieve a good trade-off between speed and accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
