Time-Series Prediction and Applications

A Machine Intelligence Approach
Softcover Nachdruck of the original 1. Auflage 2017
ISBN: 978-3-319-85435-9
Verlag: Springer International Publishing

Buch, Englisch, Band 127, 242 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 3985 g

Reihe: 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.

Zielgruppe

Research

Autoren/Hrsg.

Bhattacharya, Diptendu

Konar, Amit

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

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.