Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …

Anomaly detection within multivariate time series (MTS) is an essential task in both data
mining and service quality management. Many recent works on anomaly detection focus on …

Automatic failure diagnosis is crucial for large microservice systems. Currently, most failure
diagnosis methods rely solely on single-modal data (ie, using either metrics, logs, or traces) …

Unsupervised anomaly detection (UAD) of multivariate time series (MTS) aims to learn
robust representations of normal multivariate temporal patterns. Existing UAD methods try to …

Crash frequency model is often subject to excessive zero observation because of the rare
nature of crashes. To address the problem of imbalanced crash data, a deep generative …

Timely anomaly detection of key performance indicators (KPIs), eg, service response time,
error rate, is of utmost importance to Web services. Over the years, many unsupervised deep …

Internet-based services have seen remarkable success, generating vast amounts of
monitored key performance indicators (KPIs) as univariate or multivariate time series …

Industrial sensors capture critical information for intelligent manufacturing maintenance. To
promote equipment upgrading and manufacturing processes, intelligent decisions, and …

With the development of the internet-of-medical-things, health monitoring through
physiological signals has become a critical task. Given this opportunity, research on …

Proactive diagnosis of spacecraft issues and response to conceivable hazards has attracted
considerable interest. Hidden anomalies in satellites can cause overall system degradation …