Published by LAP LAMBERT Academic Publishing, 2010
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Published by LAP LAMBERT Academic Publishing, 2010
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Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843360413 ISBN 13: 9783843360418
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Published by LAP LAMBERT Academic Publishing, 2010
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Published by LAP LAMBERT Academic Publishing, 2010
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Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843360413 ISBN 13: 9783843360418
Language: English
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Published by LAP LAMBERT Academic Publishing Dez 2010, 2010
ISBN 10: 3843360413 ISBN 13: 9783843360418
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 164 pp. Englisch.
Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843360413 ISBN 13: 9783843360418
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
Published by LAP LAMBERT Academic Publishing Dez 2010, 2010
ISBN 10: 3843360413 ISBN 13: 9783843360418
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 164 pp. Englisch.