Time-series prediction and applications : a machine intelligence approach

• Main Authors: Konar, Amit (Author), Bhattacharya, Diptendu (Author)
• Format: Electronic eBook
• Language: English
• Published: Cham, Switzerland : Springer, 2017.
• Series: Intelligent systems reference library ; v. 127.
• Subjects: Time-series analysis > Data processing.; Machine learning.; elektronické knihy; electronic books
• ISBN: 9783319545974; 9783319545967
• ISSN: 1868-4394 ;
• Physical Description: 1 online resource

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 g...

Table of Contents:

• Preface; Acknowledgements; Contents; About the Authors; 1 An Introduction to Time-Series Prediction; Abstract; 1.1 Defining Time-Series; 1.2 Importance of Time-Series Prediction; 1.3 Hindrances in Economic Time-Series Prediction; 1.4 Machine Learning Approach to Time-Series Prediction; 1.5 Scope of Machine Learning in Time-Series Prediction; 1.6 Sources of Uncertainty in a Time-Series; 1.7 Scope of Uncertainty Management by Fuzzy Sets; 1.8 Fuzzy Time-Series; 1.8.1 Partitioning of Fuzzy Time-Series; 1.8.2 Fuzzification of a Time-Series; 1.9 Time-Series Prediction Using Fuzzy Reasoning.
• 1.10 Single and Multi-Factored Time-Series Prediction1.11 Scope of the Book; 1.12 Summary; References; 2 Self-adaptive Interval Type-2 Fuzzy Set Induced Stock Index Prediction; Abstract; 2.1 Introduction; 2.2 Preliminaries; 2.3 Proposed Approach; 2.3.1 Training Phase; 2.3.1.1 Partitioning of Main Factor Close Prices into p Intervals of Equal Length; 2.3.1.2 Construction of IT2 or Type-1 Fuzzy Sets as Appropriate for Each Interval of Close Price; 2.3.1.3 Fuzzy Prediction Rule (FPR) Construction for Consecutive {\varvec c(t) } s.
• 2.3.1.4 Grouping of IT2/T1 Fuzzy Implications for Individual Main Factor Variation {\varvec V_{M}^{d} } (t)2.3.1.5 Computing Composite Secondary Variation Series (CSVS) and Its Partitioning; 2.3.1.6 Determining Secondary to Main Factor Variation Mapping; 2.3.2 Prediction Phase; 2.3.3 Prediction with Self-adaptive IT2/T1 MFs; 2.4 Experiments; 2.4.1 Experimental Platform; 2.4.2 Experimental Modality and Results; 2.4.2.1 Policies Adopted; 2.4.2.2 MF Selection; 2.4.2.3 Adaptation Cycle; 2.4.2.4 Varying d; 2.5 Performance Analysis; 2.6 Conclusion; 2.7 Exercises; Appendix 2.1.
• Appendix 2.2: Source Codes of the ProgramsReferences; 3 Handling Main and Secondary Factors in the Antecedent for Type-2 Fuzzy Stock Prediction; Abstract; 3.1 Introduction; 3.2 Preliminaries; 3.3 Proposed Approach; 3.3.1 Method-I: Prediction Using Classical IT2FS; 3.3.2 Method-II: Secondary Factor Induced IT2 Approach; 3.3.3 Method-III: Prediction in Absence of Sufficient Data Points; 3.3.4 Method-IV: Adaptation of Membership Function in Method III to Handle Dynamic Behaviour of Time-Series [47-52]; 3.4 Experiments; 3.4.1 Experimental Platform; 3.4.2 Experimental Modality and Results.
• 3.5 ConclusionAppendix 3.1: Differential Evolution Algorithm [36, 48-50]; References; 4 Learning Structures in an Economic Time-Series for Forecasting Applications; Abstract; 4.1 Introduction; 4.2 Related Work; 4.3 DBSCAN Clustering-An Overview; 4.4 Slope-Sensitive Natural Segmentation; 4.4.1 Definitions; 4.4.2 The SSNS Algorithm; 4.5 Multi-level Clustering of Segmented Time-Blocks; 4.5.1 Pre-processing of Temporal Segments; 4.5.2 Principles of Multi-level DBSCAN Clustering; 4.5.3 The Multi-level DBSCAN Clustering Algorithm; 4.6 Knowledge Representation Using Dynamic Stochastic Automaton.