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.

翻译：我们研究了一种新颖的时序建模方法，其灵感来源于大型预训练基础模型的成功。我们提出了FAE（基础自动编码器），这是一种基于变分自动编码器（VAEs）的、用于时序数据异常检测的基础生成式人工智能模型。所谓"基础"，是指一个在海量时序数据上预训练的模型，它能够学习复杂的时间模式，这些模式对于在先前未见过的数据集上进行精确建模、预测和异常检测非常有用。FAE利用VAEs和扩张卷积神经网络（DCNNs）构建了一个通用的单变量时序建模模型，该模型最终能够在开箱即用、零样本的异常检测应用中良好运行。我们介绍了FAE的主要概念，并在来自不同领域的多个多维时序数据集上展示了初步结果，这些数据集包括一个来自运营移动互联网服务提供商（ISP）的真实数据集，以及著名的KDD 2021异常检测数据集。