协同演化时间序列上的局部对和丛发现
Local Pair and Bundle Discovery over Co-Evolving Time Series
摘要：
时间序列勘探和挖掘在多个工业和科学领域有许多应用。在本文中，我们考虑的问题，检测局部相似对和组，称为束，在共同发展的时间序列。这些是成对或成组的连续性，其值对于至少增量连续时间戳相差不超过ε，因此指示共同的局部模式和趋势。我们首先提出了一个基线算法，该算法在所有时间戳上执行扫描线扫描以识别匹配。然后，我们提出了一个过滤验证技术，只检查候选人在明智地选择检查点的时间匹配。具体来说，我们介绍了两个块扫描算法，分别发现本地对和捆绑，利用检查点的潜力，积极修剪的搜索空间。我们通过实验评估了我们的方法对现实世界和合成数据集，证明了一个数量级的基线上的执行时间的加速。本文发表在第16届国际时空数据库研讨会（SSTD 19）上。
英文摘要：
Time series exploration and mining has many applications across several industrial and scientific domains. In this paper, we consider the problem of detecting locally similar pairs and groups, called bundles, over co-evolving time series. These are pairs or groups of subsequences whose values do not differ by more than ε for at least delta consecutive timestamps, thus indicating common local patterns and trends. We first present a baseline algorithm that performs a sweep line scan across all timestamps to identify matches. Then, we propose a filter-verification technique that only examines candidate matches at judiciously chosen checkpoints across time. Specifically, we introduce two block scanning algorithms for discovering local pairs and bundles respectively, which leverage the potential of checkpoints to aggressively prune the search space. We experimentally evaluate our methods against real-world and synthetic datasets, demonstrating a speed-up in execution time by an order of magnitude over the baseline. This paper has been published in the 16th International Symposium on Spatial and Temporal Databases (SSTD19).
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。
