Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

We introduce Chronos, a simple yet effective framework for pretrained probabilistic time
series models. Chronos tokenizes time series values using scaling and quantization into a …

Time series anomaly detection is a challenging problem due to the complex temporal
dependencies and the limited label data. Although some algorithms including both …

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield
drastically improved performance compared to unsupervised models. However, they still …

Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal …

Anomaly detection in time-series has a wide range of practical applications. While numerous
anomaly detection methods have been proposed in the literature, a recent survey concluded …

Multivariate time series anomaly detection has become an active area of research in recent
years, with Deep Learning models outperforming previous approaches on benchmark …

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web
systems rely on time series data to monitor and identify anomalies in real time, as well as to …

Self-supervised methods have gained prominence in time series anomaly detection due to
the scarcity of available annotations. Nevertheless, they typically demand extensive training …

In recent times, the rapid advancements in technology have led to a digital revolution in
urban areas, and new computing frameworks are emerging to address the current issues in …