时间序列异常检测的新视角：基于补丁的快速宽度学习系统
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
AI总结：
针对时间序列异常检测中深度学习速度慢的问题，提出基于对比补丁的宽度学习系统CPatchBLS，通过双补丁结构、对比学习和模型级集成提升检测效果，在5个真实数据集上性能优于现有方法且计算效率高。
摘要：
时间序列异常检测（TSAD）是近年来学术界和工业界的研究热点。深度学习方法凭借优异性能成为主流研究方向，但近期TSAD研究出现了新观点：由于深度学习速度慢等局限性，TSAD并非必须使用深度学习。宽度学习系统（BLS）是一种浅层网络框架，具备易优化、速度快的优势，已有研究证明其性能优于传统机器学习方法，且与深度学习方法具有竞争力。结合TSAD的研究现状，我们提出了基于对比补丁的宽度学习系统（CPatchBLS），这是对补丁技术与BLS的全新探索，为TSAD提供了新的研究视角。我们通过补丁技术和简单核扰动（SKP）构建双补丁宽度学习系统（Dual-PatchBLS）作为基础模块，利用对比学习捕捉正常与异常数据在不同表示下的差异。为弥补各类补丁操作导致的时序语义损失，我们提出具备模型级集成的CPatchBLS，借助BLS的快速特性构建模型级集成，提升模型检测能力。我们在5个真实世界时间序列异常检测数据集上验证了该方法的有效性，其性能优于已有的深度学习和机器学习方法，同时保持了较高的计算效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
