Time-Series Prediction and Applications: A Machine Intelligence Approach: 127 (Intelligent Systems Reference Library) - Tapa blanda
Libro 96 de 188: Intelligent Systems Reference Library
Sinopsis
This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series
Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers’ ability and understanding of the topics covered."Sinopsis" puede pertenecer a otra edición de este libro.
De la contraportada
This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series
Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers ability and understanding of the topics covered."Sobre este título" puede pertenecer a otra edición de este libro.
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Time-Series Prediction and Applications
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Time-Series Prediction and Applications (eng)
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Time-Series Prediction and Applications: A Machine Intelligence Approach (Intelligent Systems Reference Library, 127)
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Time-Series Prediction and Applications
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Springer International Publishing, 2018
ISBN 10: 3319854356
ISBN 13: 9783319854359
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Proposes generic solutions to the prediction of an economic time-series with alternative formulations using machine learning and type-2 fuzzy setsOffers original content and a unique presentation styleIncludes the source codes of the progra. Nº de ref. del artículo: 448759161
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Time-Series Prediction and Applications
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Springer International Publishing Jul 2018, 2018
ISBN 10: 3319854356
ISBN 13: 9783319854359
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-seriesPrimarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers' ability and understanding of the topics covered. 260 pp. Englisch. Nº de ref. del artículo: 9783319854359
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Time-Series Prediction and Applications | A Machine Intelligence Approach
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Taschenbuch. Condición: Neu. Time-Series Prediction and Applications | A Machine Intelligence Approach | Amit Konar (u. a.) | Taschenbuch | Intelligent Systems Reference Library | xviii | Englisch | 2018 | Springer | EAN 9783319854359 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 114238897
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Time-Series Prediction and Applications: A Machine Intelligence Approach: 127 (Intelligent Systems Reference Library)
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Condición: New. Softcover reprint of the original 1st ed. 2017 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26376472641
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Time-Series Prediction and Applications
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Springer, Springer Jul 2018, 2018
ISBN 10: 3319854356
ISBN 13: 9783319854359
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers¿ ability and understanding of the topics covered.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 260 pp. Englisch. Nº de ref. del artículo: 9783319854359
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Time-Series Prediction and Applications: A Machine Intelligence Approach: 127 (Intelligent Systems Reference Library)
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Time-Series Prediction and Applications : A Machine Intelligence Approach
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-seriesPrimarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers' ability and understanding of the topics covered. Nº de ref. del artículo: 9783319854359
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