Parsimonious Linear Fingerprinting for Time Series
CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE
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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.
- Anatomy and Physiology
- Numerical Mathematics