Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …

Digitalization and technologization affect numerous domains, promising advantages but
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Abstract Information fusion is one of the critical aspects in diverse fields of applications;
while the collected data may provide certain perspectives, a fusion of such data can be a …

With the advances in financial technology (FinTech) in recent years, the finance industry has
attempted to enhance the efficiency of their services through technology. The financial …

Stock market is one of the attractive domains for researchers as well as academicians. It
represents highly complex non-linear fluctuating market behaviours where traders …

Many studies have successfully used reinforcement learning (RL) to train an intelligent
agent that learns profitable trading strategies from financial market data. Most of RL trading …

As a major energy consumption system in buildings, anomaly detection on multivariate time
series monitored by sensors in HVAC systems has been a significant challenge. However …

In the financial market, commodity prices change over time, yielding profit opportunities.
Various trading strategies have been proposed to yield good earnings. Pairs trading is one …

For debugging and verification of computer vision convolutional deep neural networks
(CNNs) human inspection of the learned latent representations is imperative. Therefore …