Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
This addresses anomaly detection in time-series data for applications such as network monitoring, but it is incremental as it builds on existing VAE and foundation model concepts.
The paper tackles time-series anomaly detection by introducing FAE, a foundation generative-AI model based on Variational Auto-Encoders and Dilated Convolutional Neural Networks, achieving preliminary results on datasets like KDD 2021 and a mobile ISP dataset.
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.