Overview
- Proposes generic solutions to the prediction of an economic time-series with alternative formulations using machine learning and type-2 fuzzy sets
- Offers original content and a unique presentation style
- Includes the source codes of the programs developed in MATLAB to accompany the book
- Requires a only a high-school understanding of algebra and calculus, and first-year-undergraduate-level programming skills
- Includes supplementary material: sn.pub/extras
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 127)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.Authors and Affiliations
Bibliographic Information
Book Title: Time-Series Prediction and Applications
Book Subtitle: A Machine Intelligence Approach
Authors: Amit Konar, Diptendu Bhattacharya
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-54597-4
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2017
Hardcover ISBN: 978-3-319-54596-7Published: 03 April 2017
Softcover ISBN: 978-3-319-85435-9Published: 21 July 2018
eBook ISBN: 978-3-319-54597-4Published: 25 March 2017
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVIII, 242
Number of Illustrations: 56 b/w illustrations, 13 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Computational Mathematics and Numerical Analysis