Time-Series Prediction and Applications: A Machine Intelligence Approach eBook

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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.

This is a digital product.

Time-Series Prediction and Applications: A Machine Intelligence Approach is written by Amit Konar; Diptendu Bhattacharya and published by Springer. The Digital and eTextbook ISBNs for Time-Series Prediction and Applications are 9783319545974, 3319545973 and the print ISBNs are 9783319545967, 3319545965.