Time Series Mining

Data Mining in Time Series and Streaming Databases  eBooks & eLearning

Posted by Underaglassmoon at May 20, 2018
Data Mining in Time Series and Streaming Databases

Data Mining in Time Series and Streaming Databases
World Scientific | English | 2018 | ISBN-10: 9813228032 | 196 pages | PDF | 5.96 MB

by Mark Last (Author, Editor), Horst Bunke (Editor), Abraham Kandel (Editor)

Practical Time Series Forecasting: A Hands-On Guide (2nd Edition)  eBooks & eLearning

Posted by arundhati at July 6, 2016
Practical Time Series Forecasting: A Hands-On Guide (2nd Edition)

Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide (2nd Edition)"
2012 | ISBN-10: 1468053450 | 202 pages | EPUB | 5 MB

Data Mining In Time Series Databases (Repost)  eBooks & eLearning

Posted by step778 at Feb. 2, 2016
Data Mining In Time Series Databases (Repost)

Mark Last, Abraham Kandel, Horst Bunke, "Data Mining In Time Series Databases"
2004 | pages: 205 | ISBN: 9812382909 | PDF | 4 mb
Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other... (repost)

Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects by Dawn E. Holmes, Lakhmi C. Jain
English | ISBN: 364223240X | 2012 | PDF | 264 pages | 6 MB

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other... (repost)

Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects by Dawn E. Holmes, Lakhmi C. Jain
English | ISBN: 364223240X | 2012 | PDF | 264 pages | 6 MB

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Data Mining In Time Series Databases (Repost)  eBooks & eLearning

Posted by Grev27 at Nov. 7, 2012
Data Mining In Time Series Databases (Repost)

Mark Last, Abraham Kandel, Horst Bunke, "Data Mining In Time Series Databases"
English | ISBN: 9812382909 | 2004 | PDF | 205 pages | 3,96 mb
Data Mining: Foundations and Intelligent Paradigms: Volume 3: Statistical, Bayesian, Time Series and other Theoretical Aspects

Dawn E. Holmes, Lakhmi C. Jain, "Data Mining: Foundations and Intelligent Paradigms: Volume 3: Statistical, Bayesian, Time Series and other Theoretical Aspects"
Publisher: S.ri…r | ISBN: 3642231500 | 2012 | PDF | 380 pages | 7 MB
Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects

Dawn E. Holmes, Lakhmi C. Jain, "Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects"
Publisher: S.ri…r | ISBN: 364223240X | 2012 | PDF | 264 pages | 7 MB
Data Mining In Time Series Databases (Series in Machine Perception and Artificial Intelligence)

Data Mining In Time Series Databases (Series in Machine Perception and Artificial Intelligence)
9789812382900 (9812382909) | World Scientific Publishing, 2004 | 2 MB | RS | FF

Data Mining In Time Series Databases  eBooks & eLearning

Posted by Sonora at Nov. 12, 2006
Data Mining In Time Series Databases

Data Mining In Time Series Databases
edited by Mark Last, Abraham Kandel, Horst Bunke
World Scientific | ISBN 981-238-290-9 | 2004 | PDF | 192 pages | 2.5 MB

Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed.