ASTRIDE: Adaptive Symbolization for Time Series Databases

TL;DR

ASTRIDE introduces an adaptive symbolic representation for time series that improves upon existing methods by using change-point detection and quantile-based quantization, with an accelerated variant FASTRIDE for faster processing.

Contribution

The paper presents ASTRIDE and FASTRIDE, novel adaptive symbolic representations for time series, including a new similarity measure, outperforming existing methods on classification and reconstruction tasks.

Findings

ASTRIDE outperforms SAX, 1d-SAX, SFA, and ABBA in accuracy.

FASTRIDE offers faster processing with comparable performance.

The methods are validated on 86 UCR datasets.

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.