This book provides new approaches to forecast time series, based on data mining techniques. It explores several clustering algorithms and proposes methods to exploit their strengths when dealing with temporal data. This work proposes thus a brand new philosophy to forecast any kind of time series, as all introduced methodologies claim to be general- purpose. In fact, more than one hundred real-world time series have been analyzed with great accuracy.
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This book provides new approaches to forecast time series, based on data mining techniques. It explores several clustering algorithms and proposes methods to exploit their strengths when dealing with temporal data. This work proposes thus a brand new philosophy to forecast any kind of time series, as all introduced methodologies claim to be general- purpose. In fact, more than one hundred real-world time series have been analyzed with great accuracy.
Francisco Martínez Álvarez received the MSc degree in Telecommunications Engineering from the University of Seville, and the PhD degree in Computer Engineering from the Pablo de Olavide University. He has been with the Department of Computer Science at the Pablo de Olavide University since 2007, where he is currently an Associate Professor.
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