The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Portfolio formation in financial markets is the task of not taking non-necessary risks.
Quantitative investment powered by machine learning has opened many new opportunities …

Real-world data streams pose a unique challenge to the implementation of machine
learning (ML) models and data analysis. A notable problem that has been introduced by the …

Time series prediction has been widely applied to the finance industry in applications such
as stock market price and commodity price forecasting. Machine learning methods have …

Purpose This paper aims to analyze the role and performance of different artificial
intelligence (AI) algorithms in forecasting future movements in the main indices of the world's …

The investment of time and resources for develo** better strategies is key to dealing with
future pandemics. In this work, we recreated the situation of COVID‐19 across the year …

In current scenario when economic and risk management sectors need accurate predictions
of enterprise bankruptcy, it is very importance issue to research in the field of security of …

The main problem associated with the random forest (RF) algorithm is its application of
random feature subset selection technique over a single vector. In this technique, the …

We review recent articles that focus on the main issues identified in high-frequency financial
data analysis. The issues to be addressed include nonstationarity, low signal-to-noise ratios …