The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the last decade. We believe the reason why rule discovery in real-valued time series has failed thus far is because most efforts have more or less indiscriminately applied the ideas of symbolic stream rule discovery to real-valued rule discovery. We feel that the lack of progress in this pursuit can be attributed to two related factors: the lack of effective algorithms for rule discovery in one dimensional time series, resulting in poor-quality and random rules; less accurate classifiers built for multi-dimensional time series in order to make accurate predictions. In this book, we strive to solve these problems and we introduce novel algorithms that allow us to quickly discover high quality rules in very large datasets that accurately predict the occurrence of future events.

About the Author

Mohammad is currently a Data Scientist at Apple in Cupertino, California. He earned his PhD in CS from the University of California, Riverside in 2015.Dr. Keogh is a professor of CS at UC Riverside. He is ranked among the top research leaders in Data Mining with more than 200 publications.

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