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Time-series prediction and applicati...

Bhattacharya, Diptendu.

Time-series prediction and applicationsa machine intelligence approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time-series prediction and applicationsby Amit Konar, Diptendu Bhattacharya.
Reminder of title:	a machine intelligence approach /
Author:	Konar, Amit.
other author:	Bhattacharya, Diptendu.
Published:	Cham :Springer International Publishing :2017.
Description:	xviii, 242 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Time-series analysisData processing.
Online resource:	http://dx.doi.org/10.1007/978-3-319-54597-4
ISBN:	9783319545974$q(electronic bk.)

Time-series prediction and applicationsa machine intelligence approach /
Konar, Amit.

Time-series prediction and applicationsa machine intelligence approach /[electronic resource] :by Amit Konar, Diptendu Bhattacharya. - Cham :Springer International Publishing :2017. - xviii, 242 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1271868-4394 ;. - Intelligent systems reference library ;v.24..

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.

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.

ISBN: 9783319545974$q(electronic bk.)

Standard No.: 10.1007/978-3-319-54597-4doiSubjects--Topical Terms:

190935
Time-series analysis
--Data processing.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Time-series prediction and applicationsa machine intelligence approach /
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505 0 $a 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.
520 $a 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.
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