Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

Financial markets play an important role on the economical and social organization of
modern society. In these kinds of markets, information is an invaluable asset. However, with …

The application of Artificial Intelligence (AI) to financial investment is a research area that
has attracted extensive research attention since the 1990s, when there was an accelerated …

In finance, it is believed that market information, namely, fundamentals and news
information, affects stock movements. Such media-aware stock movements essentially …

Density peak (DP) and density-based spatial clustering of applications with noise (DBSCAN)
are the representative clustering algorithms on the basis of density in unsupervised learning …

The advancements in machine learning (ML) techniques open new opportunities for
analysing production system dynamics and augmenting the domain expert's decision …

The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on
randomness in volatility series of world major markets and to examine its effect on their …

Abstract Dynamic Time Warping (DTW) is a popular and efficient distance measure used in
classification and clustering algorithms applied to time series data. By computing the DTW …

Clustering of subsequence time series remains an open issue in time series clustering.
Subsequence time series clustering is used in different fields, such as e‐commerce, outlier …

Stock return comovement analysis is important to financial analysts, decision makers, and
academic researchers and has many financial implications, such as portfolio management …