A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System

TL;DR

This paper introduces CPatchBLS, a novel patch-based broad learning system for time series anomaly detection that is faster than deep learning methods and achieves superior accuracy on real-world datasets.

Contribution

The paper proposes a new patching technique combined with BLS and contrastive learning, offering a faster and effective alternative for TSAD.

Findings

Outperforms previous deep learning and machine learning methods.

Maintains high computational efficiency.

Validated on five real-world datasets.

Abstract

Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction due to their excellent performance. However, new viewpoints have emerged in recent TSAD research. Deep learning is not required for TSAD due to limitations such as slow deep learning speed. The Broad Learning System (BLS) is a shallow network framework that benefits from its ease of optimization and speed. It has been shown to outperform machine learning approaches while remaining competitive with deep learning. Based on the current situation of TSAD, we propose the Contrastive Patch-based Broad Learning System (CPatchBLS). This is a new exploration of patching technique and BLS, providing a new perspective for TSAD. We construct Dual-PatchBLS as a base through patching and Simple Kernel Perturbation (SKP) and utilize contrastive learning to capture the differences between normal and abnormal data under different representations. To compensate for the temporal semantic loss caused by various patching, we propose CPatchBLS with model level integration, which takes advantage of BLS's fast feature to build model-level integration and improve model detection. Using five real-world series anomaly detection datasets, we confirmed the method's efficacy, outperforming previous deep learning and machine learning methods while retaining a high level of computing efficiency.