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 …

Detecting anomalies in real-world multivariate time series data is challenging due to
complex temporal dependencies and inter-variable correlations. Recently, reconstruction …

Anomaly detection is a fundamental data analytics task across scientific fields and
industries. In recent years, an increasing interest has been shown in the application of …

Anomaly detection is a fundamental task for time-series analytics with important implications
for the downstream performance of many applications. Despite increasing academic interest …

The field of time series anomaly detection is constantly advancing, with several methods
available, making it a challenge to determine the most appropriate method for a specific …

We introduce MOMENT, a family of open-source foundation models for general-purpose
time series analysis. Pre-training large models on time series data is challenging due to (1) …

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

Time-series anomaly detection is a fundamental task across scientific fields and industries.
However, the field has long faced the``elephant in the room:''critical issues including flawed …

Anomalies are rare events, and this affects the design flow of detectors that monitor systems
that behave normally most of the time but whose failure may have serious consequences …

Additive manufacturing (AM), particularly the fused filament fabrication (FFF) process,
enables the production of personalized products with unique features. However, the FFF …