Time-Series Prediction and Applications
A Machine Intelligence Approach
Published on 25. March 2017
XVIII, 242 pages
978-3-319-54597-4 (ISBN)
Description
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.More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XVIII, 242 p. 69 illus., 13 illus. in color.
File size
5,38 MB
ISBN-13
978-3-319-54597-4 (9783319545974)
DOI
10.1007/978-3-319-54597-4
Schweitzer Classification
Other editions
Additional editions
Amit Konar | Diptendu Bhattacharya
Time-Series Prediction and Applications
A Machine Intelligence Approach
Book
04/2017
Springer
€171.19
Shipment within 10-15 days
Content
An Introduction to Time-Series Prediction.- Prediction Using Self-Adaptive Interval Type-2 Fuzzy Sets.- Handling Multiple Factors in the Antecedent of Type-2 Fuzzy Rules.- Learning Structures in an Economic Time-Series for Forecasting Applications.- Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-induced Neural Regression.- Conclusions and Future Directions.
