Time-Series Prediction and Applications
A Machine Intelligence Approach
Nonfiction, Science & Nature, Mathematics, Counting & Numeration, Computers, Advanced Computing, Artificial Intelligence, General Computing
| Author: | Amit Konar, Diptendu Bhattacharya | ISBN: | 9783319545974 |
| Publisher: | Springer International Publishing | Publication: | March 25, 2017 |
| Imprint: | Springer | Language: | English |
| Author: | Amit Konar, Diptendu Bhattacharya |
| ISBN: | 9783319545974 |
| Publisher: | Springer International Publishing |
| Publication: | March 25, 2017 |
| Imprint: | Springer |
| Language: | English |
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 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.
