Unsupervised anomaly detection in time-series has been extensively investigated in the
literature. Notwithstanding the relevance of this topic in numerous application fields, a …

Anomaly detection plays a critical role in various domains, including cybersecurity, finance,
healthcare, and industrial monitoring by identifying unusual patterns or events that deviate …

While existing reconstruction-based multivariate time series (MTS) anomaly detection
methods demonstrate advanced performance on many challenging real-world datasets, they …

This article presents an algorithmic framework for real-time condition monitoring and state
forecasting using multivariate data demonstrated on thermal imagery data of a ship's engine …

In the context of rapid digitization in industrial environments, how effective are advanced
unsupervised learning models, particularly hybrid autoencoder models, at detecting …

In the era of data-driven decision-making, multisensor systems acquire complex, high-
dimensional streams capturing temporal dynamics, and multivariate time series anomaly …

The development of IoT-based sensor systems, such as many control systems in industries,
is essential, and sensors are essential for detecting chemical and biological threats …

Anomaly detection stands as a crucial aspect of time series analysis, aiming to identify
abnormal events in time series samples. The central challenge of this task lies in effectively …

Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze
anomalous behavior in complex systems, which is particularly important in fields such as …

In this paper, we propose a novel modular architecture for self-supervised multi-sensor
anomaly detection and localization. The framework consists of a spatio-temporal encoder for …