Browsing by Autor "Gissel Velarde"

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    A Machine Learning Approach for Bitcoin Forecasting
    (2023) Stefano Sossi-Rojas; Gissel Velarde; Damian Zięba
    Bitcoin is one of the cryptocurrencies that has gained popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast its future level, and other price-related features are necessary to improve forecast accuracy. We introduce a new set of time series and demonstrate that a subset is necessary to improve directional accuracy based on a machine learning ensemble. In our experiments, we study which time series and machine learning algorithms deliver the best results. We found that the most relevant time series that contribute to improving directional accuracy are open, high, and low, with the largest contribution of low in combination with an ensemble of a gated recurrent unit network and a baseline forecast. The relevance of other Bitcoin-related features that are not price-related is negligible. The proposed method delivers similar performance to the state of the art when observing directional accuracy.
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    Artificial Era
    (2023) Gissel Velarde
    Abstract The book presents a groundbreaking view of technology trends and their impact on society. It focuses mainly on the impact of Artificial Intelligence and human development through education and innovation. It introduces the waves of the Surreal Technological Progress during the Artificial Era, and announces a possible Artificial Cataclysm in the next years. There is an extensive debate about the positive and negative consequences of technological innovations. Thus, the book presents different opinions and statistics. It reviews concepts of Artificial Intelligence and Intelligence in general. Besides, it explains why many countries are developing an Artificial Intelligence strategy, and how companies and startups can acquire the technology. It investigates why technological jobs are male-dominated in several regions of the world and its consequences for society, considering gender gaps and lack of diversity. It also reviews educational paradigms that might help human development. It finally explains the human motivation for prediction, presents opinions and predictions about the future, as well as possible strategies to cope with it in a highly automated world.
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    Cyber Security Data Science: Machine Learning Methods and Their Performance on Imbalanced Datasets
    (Springer International Publishing, 2025) Mateo Lopez-Ledezma; Gissel Velarde