Machine Learning Techniques for Space Weather provides a thorough
and accessible presentation of machine learning techniques that can
be employed by space weather professionals. Additionally, it
presents an overview of real-world applications in space science to
the machine learning community, offering a bridge between the
fields. As this volume demonstrates, real advances in space weather
can be gained using nontraditional approaches that take into
account nonlinear and complex dynamics, including information
theory, nonlinear auto-regression models, neural networks and
clustering algorithms. Offering practical techniques for
translating the huge amount of information hidden in data into
useful knowledge that allows for better prediction, this book is a
unique and important resource for space physicists, space weather
professionals and computer scientists in related fields.
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