Time-Series Prediction and Applications: A Machine Intelligence Approach

by Amit Konar, Diptendu Bhattacharya

Overview

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 shastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a sk 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 sk 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.

Product Details

ISBN-13:	9783319545967
Publisher:	Springer International Publishing
Publication date:	03/26/2017
Series:	Intelligent Systems Reference Library , #127
Edition description:	1st ed. 2017
Pages:	242
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

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.