用快速寻峰与矩阵轮廓结合，高效找出长时序中的代表性片段。
QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile
- 结合快速寻峰与矩阵轮廓，密度引导聚类时间序列子段。
- 空间复杂度优于当前最优方法，可处理长时序数据。
- 适合需要可视化和总结大量时序数据的研究者使用。
在众多领域中，从长时序数据中寻找代表性波形具有科学与实际价值，有助于数据的摘要与可视化，并支持分类、预测等下游任务。本文提出QSMP方法，通过密度引导的子序列聚类来识别长时序中的代表性波形。该方法首次将快速寻峰（Quick Shift）这一找模式算法与矩阵轮廓（Matrix Profile）——一种时序相似性搜索的数据结构相结合，使快速寻峰适用于长时序子序列的聚类，且空间复杂度优于现有最优方法。在合成与真实数据集上的实验表明，QSMP能有效提取代表性波形，是进行长时序数据摘要与可视化的有力工具。
原文摘要 · Abstract (English)
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.
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