用预训练生成模型检测时间序列异常，无需调参即可用。
Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
- 基于变分自编码器和空洞卷积网络构建通用时间序列模型。
- 在多个领域数据集上实现零样本异常检测，包括真实移动运营商数据。
- 预训练后可直接用于新数据，适合工业级实时监控场景。
我们探索一种新型时间序列建模方法，受大型预训练基础模型成功的启发。提出FAE（Foundation Auto-Encoders），一种基于变分自编码器（VAEs）的生成式AI模型，用于时间序列异常检测。所谓‘基础’，指在海量时间序列数据上预训练的模型，能学习复杂的时间模式，适用于未见过的数据集上的精确建模、预测与异常检测。FAE结合变分自编码器与空洞卷积神经网络（DCNNs），构建适用于单变量时间序列的通用模型，最终可实现开箱即用的零样本异常检测。本文介绍FAE的核心概念，并在多个跨领域多维时间序列数据集上展示初步结果，包括一个真实移动运营商运营数据集及著名的KDD 2021异常检测数据集。
原文摘要 · Abstract (English)
We investigate a novel approach to time-series modeling, inspired by the successes of large pretrained foundation models. We introduce FAE (Foundation Auto-Encoders), a foundation generative-AI model for anomaly detection in time-series data, based on Variational Auto-Encoders (VAEs). By foundation, we mean a model pretrained on massive amounts of time-series data which can learn complex temporal patterns useful for accurate modeling, forecasting, and detection of anomalies on previously unseen datasets. FAE leverages VAEs and Dilated Convolutional Neural Networks (DCNNs) to build a generic model for univariate time-series modeling, which could eventually perform properly in out-of-the-box, zero-shot anomaly detection applications. We introduce the main concepts of FAE, and present preliminary results in different multi-dimensional time-series datasets from various domains, including a real dataset from an operational mobile ISP, and the well known KDD 2021 Anomaly Detection dataset.
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