ASTRIDE: Adaptive Symbolization for Time Series Databases

• URL: http://arxiv.org/abs/2302.04097v1
• Date: Wed, 8 Feb 2023 14:46:24 GMT
• Title: ASTRIDE: Adaptive Symbolization for Time Series Databases
• Authors: Sylvain W. Combettes, Charles Truong, and Laurent Oudre
• Abstract summary: We introduce ASTRIDE, a novel symbolic representation of time series, along with its accelerated variant FASTRIDE (Fast ASTRIDE) Unlike most symbolization procedures, ASTRIDE is adaptive during both the segmentation step by performing change-point detection and the quantization step by using quantiles. We demonstrate the performance of the ASTRIDE and FASTRIDE representations compared to SAX, 1d-SAX, SFA, and ABBA on reconstruction and, when applicable, on classification tasks.
• Score: 6.8820425565516095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce ASTRIDE (Adaptive Symbolization for Time seRIes DatabasEs), a novel symbolic representation of time series, along with its accelerated variant FASTRIDE (Fast ASTRIDE). Unlike most symbolization procedures, ASTRIDE is adaptive during both the segmentation step by performing change-point detection and the quantization step by using quantiles. Instead of proceeding signal by signal, ASTRIDE builds a dictionary of symbols that is common to all signals in a data set. We also introduce D-GED (Dynamic General Edit Distance), a novel similarity measure on symbolic representations based on the general edit distance. We demonstrate the performance of the ASTRIDE and FASTRIDE representations compared to SAX (Symbolic Aggregate approXimation), 1d-SAX, SFA (Symbolic Fourier Approximation), and ABBA (Adaptive Brownian Bridge-based Aggregation) on reconstruction and, when applicable, on classification tasks. These algorithms are evaluated on 86 univariate equal-size data sets from the UCR Time Series Classification Archive. An open source GitHub repository called astride is made available to reproduce all the experiments in Python.

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