标题： 面向时间序列异常检测的基础自动编码器 显示英文标题

标题： Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

摘要： 我们研究一种新颖的时间序列建模方法，该方法受到大型预训练基础模型成功经验的启发。 我们引入了FAE（基础自动编码器），这是一种用于时间序列数据异常检测的基础生成式AI模型，基于变分自动编码器（VAEs）。 所谓基础，我们指的是在大量时间序列数据上预训练的模型，它可以学习复杂的时序模式，有助于在以前未见过的数据集上进行准确的建模、预测和异常检测。 FAE利用VAEs和扩张卷积神经网络（DCNNs）构建一个通用的单变量时间序列建模模型，该模型最终可以在开箱即用、零样本异常检测应用中正常工作。 我们介绍了FAE的主要概念，并在来自不同领域的多种多维时间序列数据集上展示了初步结果，包括一个来自运营移动ISP的真实数据集，以及著名的KDD 2021异常检测数据集。 显示英文摘要

摘要： 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.