Increasingly large data series collections are becoming commonplace across many different domains and applications. A key operation in the analysis of data series collections is similarity search, which has attracted lots of attention and effort over the past two decades. Even though several relevant approaches have been proposed in the literature, none of the existing studies provides a detailed evaluation against the available alternatives. The lack of comparative results is further exacerbated by the non-standard use of terminology, which has led to confusion and misconceptions. In this paper, we provide definitions for the different flavors of similarity search that have been studied in the past, and present the first systematic experimental evaluation of the efficiency of data series similarity search techniques. Based on the experimental results, we describe the strengths and weaknesses of each approach and give recommendations for the best approach to use under typical use cases. Finally, by identifying the shortcomings of each method, our findings lay the ground for solid further developments in the field.

中文翻译：

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数据系列相似性搜索的 Lernaean Hydra：最新技术的实验评估

越来越大的数据系列集合在许多不同的领域和应用程序中变得司空见惯。数据序列集合分析中的一个关键操作是相似性搜索，这在过去的二十年中引起了很多关注和努力。尽管文献中提出了几种相关的方法，但现有研究中没有一项针对可用的替代方案提供详细的评估。术语的非标准使用进一步加剧了比较结果的缺乏，从而导致混淆和误解。在本文中，我们为过去研究过的不同风格的相似性搜索提供了定义，并首次对数据系列相似性搜索技术的效率进行了系统的实验评估。根据实验结果，我们描述了每种方法的优点和缺点，并为在典型用例下使用的最佳方法提供了建议。最后，通过确定每种方法的缺点，我们的发现为该领域的进一步发展奠定了基础。