SAX Navigator：通过层次聚类分析探索时间序列
SAX Navigator: Time Series Exploration through Hierarchical Clustering
摘要：
比较许多长时间序列是具有挑战性的手工做。聚类时间序列使数据分析师能够发现多个时间序列之间的相关性和异常。然而，即使在合理的聚类之后，分析师也必须仔细检查聚类之间的相关性或聚类内的相似性。我们开发了一个交互式可视化工具SAXNavigator，它允许用户分层地探索全局模式以及跨大量时间序列数据的个体观察。我们的可视化提供了一种独特的方式来导航时间序列，涉及到一个“词汇表的模式”，通过使用降维技术，符号聚合approXimation（SAX）。使用SAX，时间序列数据可以高效地进行聚类，并且可以更快地进行大规模查询。我们展示了SAX导航分析模式的能力，在大型时间序列数据的基础上，三个案例研究的天文数据集。我们验证了我们的系统的可用性，通过有声思维的研究与天文学领域的科学家。
英文摘要：
Comparing many long time series is challenging to do by hand. Clustering time series enables data analysts to discover relevance between and anomalies among multiple time series. However, even after reasonable clustering, analysts have to scrutinize correlations between clusters or similarities within a cluster. We developed SAX Navigator, an interactive visualization tool, that allows users to hierarchically explore global patterns as well as individual observations across large collections of time series data. Our visualization provides a unique way to navigate time series that involves a "vocabulary of patterns" developed by using a dimensionality reduction technique,Symbolic Aggregate approXimation(SAX). With SAX, the time series data clusters efficiently and is quicker to query at scale. We demonstrate the ability of SAX Navigator to analyze patterns in large time series data based on three case studies for an astronomy data set. We verify the usability of our system through a think-aloud study with an astronomy domain scientist.
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。
