High-performance Time Series Anomaly Discovery on Graphics Processors

TL;DR

This paper introduces PALMAD, a GPU-based parallel algorithm that significantly accelerates the discovery of time series discords, improving performance over existing methods and enabling efficient anomaly detection in large datasets.

Contribution

The paper presents a novel GPU parallelization scheme for the MERLIN algorithm, enhancing anomaly discovery speed through optimized recurrent formulas and data structures.

Findings

PALMAD outperforms parallel analogs in experiments.

Efficient anomaly detection in real-world time series.

Discord heatmap technique visualizes anomalies effectively.

Abstract

Currently, discovering subsequence anomalies in time series remains one of the most topical research problems. A subsequence anomaly refers to successive points in time that are collectively abnormal, although each point is not necessarily an outlier. Among a large number of approaches to discovering subsequence anomalies, the discord concept is considered one of the best. A time series discord is intuitively defined as a subsequence of a given length that is maximally far away from its non-overlapping nearest neighbor. Recently introduced the MERLIN algorithm discovers time series discords of every possible length in a specified range, thereby eliminating the need to set even that sole parameter to discover discords in a time series. However, MERLIN is serial and its parallelization could increase the performance of discords discovery. In this article, we introduce a novel parallelization scheme for GPUs, called PALMAD, Parallel Arbitrary Length MERLIN-based Anomaly Discovery. As opposed to its serial predecessor, PALMAD employs recurrent formulas we have derived to avoid redundant calculations, and advanced data structures for the efficient implementation of parallel processing. Experimental evaluation over real-world and synthetic time series shows that our algorithm outperforms parallel analogs. We also apply PALMAD to discover anomalies in a real-world time series employing our proposed discord heatmap technique to illustrate the results.