Applications of Mining Massive Time Series Data - Couverture souple

Shokoohi-Yekta, Mohammad; Keogh, Eamonn

 
9783659761584: Applications of Mining Massive Time Series Data
Couverture souple
ISBN 10 : 3659761583 ISBN 13 : 9783659761584
Editeur : LAP LAMBERT Academic Publishing, 2015

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2015
Langue
anglais
ISBN 10 
3659761583
ISBN 13 
9783659761584
Reliure
Broché
Nombre de pages
124
Coordonnées du fabricant
non disponible
Personne responsable
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