Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Credit card fraud is becoming a serious and growing problem as a result of the emergence
of innovative technologies and communication methods, such as contactless payment. In …

Financial time series analysis plays a central role in hedging market risks and optimizing
investment decisions. This is a challenging task as the problems are always accompanied …

As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Financial fraud is a persistent problem in the finance industry that may have severe
consequences for individuals, businesses, and economies. Graph Neural Networks (GNNs) …

Credit card fraud incurs a considerable cost for both cardholders and issuing banks.
Contemporary methods apply machine learning-based classifiers to detect fraudulent …

The price movement prediction of stock market has been a classical yet challenging
problem, with the attention of both economists and computer scientists. In recent years …

Abstract Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving
graph analytical problems in various real-world applications. Nevertheless, GNNs could …

The majority of existing anomaly detection methods for multivariate time series are based on
Transformers and Autoencoders owing to their superior capabilities. However, these …

The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …