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Signed path dependence in financial markets
In Signed path dependence in financial markets: Applications and implications, computer scientist and academic Fabio Dias delves into cutting-edge techniques at the intersection of machine learning, time series analysis, and finance. This comprehensive guide bridges theory and application, offering readers insights into predictive modeling, algorithmic trading, and the nuanced dynamics of option pricing. Dias combines rigorous econometric methods with hands-on machine learning approaches, presenting a toolkit for anyone looking to leverage data-driven insights to navigate and predict complex financial markets. An essential read for practitioners, researchers, and students of financial engineering and quantitative finance.
Comparative Research in Adult Education
The volume presents research that emerges from the 9th international Adult Education Academy (2022), which brings together researchers, students and practitioners from around the world to share perspectives comparatively. More than 80 participants from almost 20 different countries have exchanged, compared and expanded their individual knowledge and experience on adult learning and education. This volume consisting of eight contributions (including one fundamental article beforehand) assumes that globalisation affects national, regional and local levels of adult learning and education. Transformational relations are observed and analysed through the lens of participation, sustainability and digitalisation. All contributions apply an international comparative research approach to empirically investigate these areas with their upcoming needs. This approach takes place under consideration of comparison as a research method which not only grounds on a long tradition and relies on a set of rules and techniques, but also on an inner attitude and sensitivity with which we look at the world and its global needs while trying to understand.
Solar Irradiance and Photovoltaic Power Forecasting
Forecasting plays an indispensable role in grid integration of solar energy, which is an important pathway toward the grand goal of achieving planetary carbon neutrality. This rather specialized field of solar forecasting constitutes both irradiance and photovoltaic power forecasting. Its dependence on atmospheric sciences and implications for power system operations and planning make the multi-disciplinary nature of solar forecasting immediately obvious. Advances in solar forecasting represent a quiet revolution, as the landscape of solar forecasting research and practice has dramatically advanced as compared to just a decade ago. Solar Irradiance and Photovoltaic Power Forecasting provides...
Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning
In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time.
Problem-Based Learning: A Didactic Strategy in the Teaching of System Simulation
This book describes and outlines the theoretical foundations of system simulation in teaching, and as a practical contribution to teaching-and-learning models. It presents various methodologies used in teaching, the goal being to solve real-life problems by creating simulation models and probability distributions that allow correlations to be drawn between a real model and a simulated model. Moreover, the book demonstrates the role of simulation in decision-making processes connected to teaching and learning.
Objective identification and climatology of mesoscale high-wind features within extratropical cyclones
Strong winds accompanying extratropical cyclones are commonly associated with various mesoscale features. This work introduces RAMEFI (RAndom-forest-based Mesoscale wind Feature Identification), an objective and flexible identification approach based on key surface characteristics to distinguish these features. RAMEFI is further applied to compile a climatology over Europe, offering a comprehensive analysis of feature frequency, distribution, and characteristics.
Old Maryland
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c't innovate
Für c't innovate haben sich das Computermagazin c't und das Wissenschaftsmagazin Technology Review zusammengetan. Die erste Co-Produktion richtet sich an alle, für die Innovation mehr ist als das neuste Smartphone-Modell. An Technik-Interessierte, die über den Tellerrand der IT hinausblicken wollen, und die technischen Trends von morgen nicht nur rechtzeitig erkennen, sondern auch verstehen und nutzen wollen. So wird die ausführliche Analyse der Stärken und Schwächen von Wasserstoff- und batteriebetriebenen Autos ergänzt durch eine Marktübersicht verfügbarer Elektroautos. Ein Themenschwerpunkt über die zehn wichtigsten Algorithmen erklärt nicht nur, wie Deep Learning, Googles Navi...
Bulletin
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