Time-Series Prediction and Applications: A Machine Intelligence Approach: 127 (Intelligent Systems Reference Library, 127) - Image 1

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

Name: Time-Series Prediction and Applications: A Machine Intelligence Approach: 127 (Intelligent Systems Reference Library, 127)
Brand: Springer
Price: 120.94 GBP
Availability: InStock

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Description

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. From the Back Cover 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.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3319854356
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 21 July 2018
Listed Since: 21 July 2018

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

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