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 …

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 …

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 …

In the ever-evolving field of time series analysis, detecting changes in patterns and
dynamics is paramount for accurate forecasting and meaningful insights. This article …

In this work, we focus on robust time series representation learning. Our assumption is that
real-world time series is noisy and complementary information from different views of the …

Performance anomaly alerting based on trace data plays an important role in assuring the
quality of online service systems. However, engineers find that many anomalies reported by …