Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

We look into Generative Adversarial Network (GAN), its prevalent variants and applications
in a number of sectors. GANs combine two neural networks that compete against one …

We investigate the use of Generative Adversarial Networks (GANs) for probabilistic
forecasting of financial time series. To this end, we introduce a novel economics-driven loss …

Anomaly detection has become an indispensable tool for modern society, applied in a wide
range of applications, from detecting fraudulent transactions to malignant brain tumors. Over …

Generative adversarial networks (GANs) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

Modelling in finance is a challenging task: the data often has complex statistical properties
and its inner workings are largely unknown. Deep learning algorithms are making progress …

Market manipulation through buy-and-sell has always been and persists to be a serious
challenge for market participants and supervisors, and as markets are expanded, the …

Purpose The emergence of machine learning has opened a new way for researchers. It
allows them to supplement the traditional manual methods for conducting a literature review …

In recent years, mechanical sensor data anomaly detection has gained much attention in the
machine learning and mechanical fault warning fields. Limited by the fact that there is far …