A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …

Deep learning (DL) has become a core component of modern artificial intelligence (AI),
driving significant advancements across diverse fields by facilitating the analysis of complex …

Developing a generative model of realistic order flow in financial markets is a challenging
open problem, with numerous applications for market participants. Addressing this, we …

The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

Machine learning (ML) has transformed the financial industry by enabling advanced
applications such as credit scoring, fraud detection, and market forecasting. At the core of …

The integration of generative AI (GAI) into the financial sector has brought about significant
advancements, offering new solutions for various financial tasks. This review paper provides …

This review paper examines state-of-the-art algorithms and techniques in quantum machine
learning with potential applications in finance. We discuss QML techniques in supervised …

This article explores the determinants of the adoption of artificial intelligence (AI) in small
and medium-sized enterprises (SMEs) with special attention to the impact of competencies …

When constructing portfolios, a key problem is that a lot of financial time series data are
sparse, making it challenging to apply machine learning methods. Polymodel theory can …

Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the
financial domain with its advanced data processing capabilities. This paper introduces …