Suchergebnisse

Cover -- Guest editorial: Cryptocurrencies: current trends and future perspectives -- Predicting bitcoin price movements using sentimentan alysis: a machine learning approach -- Mining netizen's opinion on cryptocurrency: sentimentan alysis of Twitter data -- Is Bitcoin a safe haven? Application of FinTech to safeguard Australian stock markets -- Price efficiency and safe-haven property of Bitcoin in relation to stocks in the pandemic era -- Dynamic frequency relationships between bitcoin, oil, gold and economic policy uncertainty index -- Analysis of diversification benefits for cryptocurrency portfolios before and during theCOVID-19 pandemic -- Time series prediction using machine learning: a case of Bitcoin returns -- Accounting for crypto-assets:stake holders' perceptions -- Investor attention and cryptocurrency price crash risk: a quantile regression approach -- Dissecting the stock to flow model for Bitcoin -- Cryptocurrencies' hashrate and electricity consumption: evidence from mining activities.

The term "big data" characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs – volume, velocity, variety, and veracity - to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-to-use distributed, scalable, and fault-tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-the-art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions.