QSMP：通过Quick Shift+Matrix Profile寻找代表性时间序列子序列
QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile
AI总结：
QSMP是结合Quick Shift与Matrix Profile的新方法，可从长时序数据中找代表性波形，空间复杂度优于现有方法，能用于长时序数据的总结与可视化。
AI中文摘要：
在许多领域中，从长时序数据中寻找代表性波形具有科学与实用价值，这一工作可实现大型时间序列数据集的总结与可视化，还能为分类、预测等下游任务提供支持。本文提出QSMP方法，通过对时间序列子序列进行密度引导的聚类，从长时序数据中寻找代表性波形。该方法将模式搜索算法Quick Shift与时间序列相似性搜索数据结构Matrix Profile建立新颖关联，使Quick Shift适配长时序子序列的聚类任务，且空间复杂度优于现有最优方法。在合成数据集与真实数据集上的实验表明，QSMP可通过寻找代表性波形，成为总结与可视化长时序数据的有效工具。
英文摘要：
Finding representative waveforms in long time series has scientific and practical value in many domains, as it enables summarization and visualization of large time series datasets, and downstream tasks like classification and forecasting. We present here QSMP, a method to find representative waveforms in long time series through a density-guided clustering of time series subsequences. Our method makes a novel connection between Quick Shift, a mode-seeking algorithm, and the Matrix Profile, a time series similarity-search data structure, to adapt Quick Shift to the clustering of subsequences in long time series, with a space complexity that is superior to the state-of-the-art method. Our experiments on synthetic and real datasets show that QSMP can be a valuable tool to summarize and visualize long time series by finding representative waveforms.
