A New Lower Bounding Paradigm and Tighter Lower Bounds for Elastic Similarity Measures

TL;DR

This paper introduces a new bipartite graph edge-cover paradigm for deriving tighter lower bounds in elastic similarity measures, significantly improving efficiency in time series similarity search and clustering.

Contribution

It proposes a novel bipartite graph-based framework and a specific instantiation, BGLB, that yields the tightest known lower bounds for multiple elastic measures, enhancing search speed.

Findings

BGLB achieves the tightest lower bounds for six elastic measures.

BGLB outperforms existing methods in nearest neighbor search speedups.

BGLB accelerates density-based clustering in time series analysis.

Abstract

Elastic similarity measures are fundamental to time series similarity search because of their ability to handle temporal misalignments. These measures are inherently computationally expensive, therefore necessitating the use of lower bounds to prune unnecessary comparisons. This paper proposes a new \emph{Bipartite Graph Edge-Cover Paradigm} for deriving lower bounds, which applies to a broad class of elastic similarity measures. This paradigm formulates lower bounding as a vertex-weighting problem on a weighted bipartite graph induced from the input time series. Under this paradigm, most of the existing lower bounds of elastic similarity measures can be viewed as simple instantiations. We further propose \textit{BGLB}, an instantiation of the proposed paradigm that incorporates an additional augmentation term, yielding lower bounds that are provably tighter. Theoretical analysis and extensive experiments on 128 real-world datasets demonstrate that \textit{BGLB} achieves the tightest known lower bounds for six elastic measures (ERP, MSM, TWED, LCSS, EDR, and SWALE). Moreover, \textit{BGLB} remains highly competitive for \textit{DTW} with a favorable trade-off between tightness and computational efficiency. In nearest neighbor search, integrating \textit{BGLB} into filter pipelines consistently outperforms state-of-the-art methods, achieving speedups ranging from $24.6\%$ to $84.9\%$ across various elastic similarity measures. Besides, \textit{BGLB} also delivers a significant acceleration in density-based clustering applications, validating the practical potential of \textit{BGLB} in time series similarity search tasks based on elastic similarity measures.