Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Time series anomaly detection is critical for a wide range of applications. It aims to identify
deviant samples from the normal sample distribution in time series. The most fundamental …

Large language model (LLM) applications in cloud root cause analysis (RCA) have been
actively explored recently. However, current methods are still reliant on manual workflow …

Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

Many unsupervised methods have recently been proposed for multivariate time series
anomaly detection. However, existing works mainly focus on stable data yet often omit the …

With the advent of the big data era, the detection of anomalies in time series data, especially
multidimensional time series data, has received a great deal of attention from researchers in …

Time series anomaly detection (TSAD) has gained significant attention due to its real-world
applications to improve the stability of modern software systems. However, there is no …

Transformer-based models have emerged as promising tools for time series forecasting.
However, these models cannot make accurate prediction for long input time series. On the …