Time-Series Prediction and Applications. A Machine Intelligence Approach

✍ Scribed by Amit Konar, Diptendu Bhattacharya

• Publisher: Springer
• Year: 2017
• Tongue: English
• Leaves: 255
• Series: Intelligent systems reference library 127
• Category: Library

✦ 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.

✦ Table of Contents

Front Matter....Pages i-xviii
An Introduction to Time-Series Prediction....Pages 1-37
Self-adaptive Interval Type-2 Fuzzy Set Induced Stock Index Prediction....Pages 39-103
Handling Main and Secondary Factors in the Antecedent for Type-2 Fuzzy Stock Prediction....Pages 105-132
Learning Structures in an Economic Time-Series for Forecasting Applications....Pages 133-188
Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-Induced Neural Regression....Pages 189-233
Conclusions....Pages 235-236
Back Matter....Pages 237-242