Time-series Prediction and Applications : A Machine Intelligence Approach
Language: English
Published by Springer, 2017
Series: Book 96 of 188 - Intelligent Systems Reference Library
- Hardcover
- Used
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Title
- Time-series Prediction and Applications : A Machine Intelligence Approach
- Author
- Konar, Amit; Bhattacharya, Diptendu
- Publisher
- Springer
- Publication year
- 2017
- Condition
- As New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319545965
- ISBN 13
- 9783319545967
- Series
- Book 96 of 188: Intelligent Systems Reference Library
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."Synopsis" may belong to another edition of this title.
From the Back Cover
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."About the title" may belong to another edition of this title.
GreatBookPricesUK
Woodford Green, United Kingdom
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