Parsimonious linear fingerprinting for time series
PVLDB(2010)
摘要
We study the problem of mining and summarizing multiple time series effectively and efficiently. We propose PLiF, a novel method to discover essential characteristics ("fingerprints"), by exploiting the joint dynamics in numerical sequences. Our fingerprinting method has the following benefits: (a) it leads to interpretable features; (b) it is versatile: PLiF enables numerous mining tasks, including clustering, compression, visualization, forecasting, and segmentation, matching top competitors in each task; and (c) it is fast and scalable, with linear complexity on the length of the sequences. We did experiments on both synthetic and real datasets, including human motion capture data (17MB of human motions), sensor data (166 sensors), and network router traffic data (18 million raw updates over 2 years). Despite its generality, PLiF outperforms the top clustering methods on clustering; the top compression methods on compression (3 times better reconstruction error, for the same compression ratio); it gives meaningful visualization and at the same time, enjoys a linear scale-up.
更多查看译文
关键词
human motion,top compression method,time series,linear complexity,top competitor,compression ratio,human motion capture data,sensor data,parsimonious linear fingerprinting,network router traffic data,fingerprinting method,top clustering method,detectors,fingerprints,feature extraction,scaling factor,mining engineering,numerical analysis,clustering,time series analysis,matching,motion
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络