MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance

TL;DR

MS-Index is a novel algorithm enabling fast, exact top-k subsequence search in multivariate time series, supporting ad-hoc channel selection and outperforming existing methods significantly.

Contribution

Introduces MS-Index, a new exact algorithm for multivariate time series subsequence search that scales efficiently with query channels and supports ad-hoc channel selection.

Findings

Outperforms state-of-the-art methods by one to two orders of magnitude.

Supports sublinear scaling with the number of query channels.

Effective on diverse datasets with both raw and normalized data.

Abstract

Modern applications frequently collect and analyze temporal data in the form of multivariate time series (MTS) -- time series that contain multiple channels. A common task in this context is subsequence search, which involves identifying all MTS that contain subsequences highly similar to a query time series. In practical scenarios, not all channels of an MTS are relevant to every query. For instance, airplane sensors may gather data on a plethora of components and subsystems, but only a few of these are relevant to a specific query, such as identifying the cause of a malfunctioning landing gear, or a specific flight maneuver. Consequently, the relevant query channels are often specified at query time. In this work, we introduce the Multivariate Subsequence Index (MS-Index), a novel algorithm for nearest neighbor MTS subsequence search under Euclidean distance that supports ad-hoc selection of query channels. The algorithm is exact and demonstrates query performance that scales sublinearly to the number of query channels. We examine the properties of \name with a thorough experimental evaluation over 34 datasets, and show that it outperforms the state-of-the-art one to two orders of magnitude for both raw and normalized subsequences.