Artificial Intelligence (AI) approaches have been increasingly used in financial markets as
technology advances. In this research paper, we conduct a Systematic Literature Review …

Operational forecasting often requires predicting collections of related, multivariate time
series data that are high-dimensional in nature. This can be tackled by fitting a single …

We demonstrate a novel application of online transfer learning for a digital assets trading
agent. This agent uses a powerful feature space representation in the form of an echo state …

Cloud–edge-device collaborative computation offloading can provide flexible and real-time
data processing services for massive resource-constrained devices in power internet of …

Reinforcement Learning (RL) has experienced significant advancement over the past
decade, prompting a growing interest in applications within finance. This survey critically …

Literature highlighted that financial time series data pose significant challenges for accurate
stock price prediction, because these data are characterized by noise and susceptibility to …

Reinforcement learning (RL) has emerged as a promising approach for developing
intelligent trading systems in the stock market. The intention of this survey article is to …

Cyber-physical systems (CPS) transform how humans interact with technology by integrating
sensing, computation, networking, and control with physical processes to facilitate smart …

Abstract Machine learning (ML) techniques have previously been applied to address the
problem of predicting financial price time series and developing automated trading …

We extend the research into cross-sectional momentum trading strategies. Our main result is
our novel ranking algorithm, the naive Bayes asset ranker (nbar), which we use to select …