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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