Matching Consecutive Subpatterns Over Streaming Time Series

TL;DR

This paper introduces a new method for efficiently matching patterns composed of multiple consecutive subpatterns in streaming time series data, addressing latency and resource constraints in Industry 4.0 and IoT applications.

Contribution

It formulates the novel problem of consecutive subpatterns matching and proposes the Equal-Length Block (ELB) representation with efficient implementations that work across all Lp-Norms without false dismissals.

Findings

Outperforms brute-force and MSM methods by orders of magnitude.

Works effectively under all Lp-Norms.

Validated on synthetic and real-world datasets.

Abstract

Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching model, a pattern may contain multiple subpatterns representing different physical meanings in the real world. Hence, we formulate a new problem, called "consecutive subpatterns matching", which allows users to specify a pattern containing several consecutive subpatterns with various specified thresholds. We propose a novel representation Equal-Length Block (ELB) together with two efficient implementations, which work very well under all Lp-Norms without false dismissals. Extensive experiments are performed on synthetic and real-world datasets to illustrate that our approach outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation by orders of magnitude.