Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

2025-07-02 · Gastón García González, Pedro Casas, Emilio Martínez, Alicia Fernández arxiv

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.

📄 PDF Abstract BibTeX arXiv:2507.01875

Code (0)

등록된 구현이 없습니다.

Tasks

Anomaly Detection

Applying Quantum Autoencoders for Time Series Anomaly Detection

2024-10-05 · Robin Frehner, Kurt Stockinger

Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition or medical diagnosis. Several algorithms have been introduced using classical computing approach…

Anomaly Detection Fraud Detection Medical Diagnosis Time Series+1

An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Version

2025-10-21 · Buang Zhang, Tung Kieu, Xiangfei Qiu, Chenjuan Guo 외 arxiv

Time series anomaly detection is important in modern large-scale systems and is applied in a variety of domains to analyze and monitor the operation of diverse systems. Unsupervised approaches have received widespread in…

Time Series Anomaly Detection

Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction

2021-10-15 · Qinfeng Xiao, Shikuan Shao, Jing Wang

Detecting anomalies for multivariate time-series without manual supervision continues a challenging problem due to the increased scale of dimensions and complexity of today's IT monitoring systems. Recent progress of uns…

Anomaly Detection Time Series Time Series Analysis Time Series Anomaly Detection+1

Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques

2023-08-06 · Anastasios Iliopoulos, John Violos, Christos Diou, Iraklis Varlamis

Anomaly Detection in multivariate time series is a major problem in many fields. Due to their nature, anomalies sparsely occur in real data, thus making the task of anomaly detection a challenging problem for classificat…

Anomaly Detection Time Series

Warped Time Series Anomaly Detection

2024-04-18 · Charlotte Lacoquelle, Xavier Pucel, Louise Travé-Massuyès, Axel Reymonet 외

This paper addresses the problem of detecting time series outliers, focusing on systems with repetitive behavior, such as industrial robots operating on production lines.Notable challenges arise from the fact that a task…

Anomaly Detection Dynamic Time Warping GPU Industrial Robots+2