Time-Series Prediction and Applications: A Machine Intelligence Approach (Intelligent Systems Reference Library, 127)

Amit Konar; Diptendu Bhattacharya

ISBN 10: 3319545965 / ISBN 13: 9783319545967

Published by Springer, 2017

About this Item

Hardcover, xviii + 242 pages, NOT ex-library. Fine interior, clean and bright throughout with unmarked text, free of inscriptions and stamps, firmly bound. Boards show regular shelfwear, small cosmetic indentations. Issued without a dust jacket.

Seller Inventory # 008212

Bibliographic Details

• Title: Time-Series Prediction and Applications: A Machine Intelligence Approach (Intelligent Systems Reference Library, 127)
• Publisher: Springer
• Publication Date: 2017
• Language: English
• ISBN 10: 3319545965
• ISBN 13: 9783319545967
• Binding: Hardcover
• Edition: 1st Edition
• Condition: Very Good

About this title

Synopsis

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.