Error-bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series

TL;DR

This paper introduces a novel dictionary-based method for efficient approximate inter-time series similarity joins with error guarantees, significantly accelerating anomaly detection while maintaining accuracy.

Contribution

It presents a new compact dictionary representation enabling fast, error-bounded approximate inter-time series joins, extending prior work focused mainly on self-joins.

Findings

Achieves at least 20X throughput improvement in anomaly mining systems.

Maintains essentially no decrease in accuracy with the new method.

Demonstrates utility across diverse domains like medicine and transportation.

Abstract

The matrix profile is an effective data mining tool that provides similarity join functionality for time series data. Users of the matrix profile can either join a time series with itself using intra-similarity join (i.e., self-join) or join a time series with another time series using inter-similarity join. By invoking either or both types of joins, the matrix profile can help users discover both conserved and anomalous structures in the data. Since the introduction of the matrix profile five years ago, multiple efforts have been made to speed up the computation with approximate joins; however, the majority of these efforts only focus on self-joins. In this work, we show that it is possible to efficiently perform approximate inter-time series similarity joins with error bounded guarantees by creating a compact "dictionary" representation of time series. Using the dictionary representation instead of the original time series, we are able to improve the throughput of an anomaly mining system by at least 20X, with essentially no decrease in accuracy. As a side effect, the dictionaries also summarize the time series in a semantically meaningful way and can provide intuitive and actionable insights. We demonstrate the utility of our dictionary-based inter-time series similarity joins on domains as diverse as medicine and transportation.