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

By: Konar, Amit.
Contributor(s): Bhattacharya, Diptendu.
Material type: TextTextSeries: eBooks on Demand.Intelligent Systems Reference Library: Publisher: Cham : Springer International Publishing, 2017Copyright date: ©2017Description: 1 online resource (255 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783319545974.Subject(s): Computer science_xMathematicsAdditional physical formats: Print version:: Time-Series Prediction and Applications : A Machine Intelligence ApproachDDC classification: 519.55 Online resources: Click here to view this ebook.
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 Prediction -- 1.11 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 Programs -- References.
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 Conclusion -- Appendix 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 -- 4.6.1 Construction of Dynamic Stochastic Automaton (DSA) -- 4.6.2 Forecasting Using the Dynamic Stochastic Automaton -- 4.7 Computational Complexity -- 4.8 Prediction Experiments and Results -- 4.9 Performance Analysis -- 4.10 Conclusion -- Appendix 4.1: Source Codes of the Programs -- References -- 5 Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-Induced Neural Regression -- Abstract -- 5.1 Introduction -- 5.2 Preliminaries -- 5.2.1 Fuzzy Sets and Time-Series Partitioning -- 5.2.2 Back-Propagation Algorithm -- 5.2.3 Radial Basis Function (RBF) Networks -- 5.3 First-Order Transition Rule Based NN Model -- 5.4 Fuzzy Rule Based NN Model.
5.5 Experiments and Results -- 5.5.1 Experiment 1: Sunspot Time-Series Prediction -- 5.5.2 Experiment 2: TAIEX Close-Price Prediction -- 5.6 Conclusion -- Appendix 5.1: Source Codes of the Programs -- References -- 6 Conclusions -- 6.1 Conclusions -- 6.2 Future Research Directions -- Index.
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Electronic Book UT Tyler Online
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TA1-2040 (Browse shelf) http://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4830420 Available EBC4830420

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 Prediction -- 1.11 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 Programs -- References.

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 Conclusion -- Appendix 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 -- 4.6.1 Construction of Dynamic Stochastic Automaton (DSA) -- 4.6.2 Forecasting Using the Dynamic Stochastic Automaton -- 4.7 Computational Complexity -- 4.8 Prediction Experiments and Results -- 4.9 Performance Analysis -- 4.10 Conclusion -- Appendix 4.1: Source Codes of the Programs -- References -- 5 Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-Induced Neural Regression -- Abstract -- 5.1 Introduction -- 5.2 Preliminaries -- 5.2.1 Fuzzy Sets and Time-Series Partitioning -- 5.2.2 Back-Propagation Algorithm -- 5.2.3 Radial Basis Function (RBF) Networks -- 5.3 First-Order Transition Rule Based NN Model -- 5.4 Fuzzy Rule Based NN Model.

5.5 Experiments and Results -- 5.5.1 Experiment 1: Sunspot Time-Series Prediction -- 5.5.2 Experiment 2: TAIEX Close-Price Prediction -- 5.6 Conclusion -- Appendix 5.1: Source Codes of the Programs -- References -- 6 Conclusions -- 6.1 Conclusions -- 6.2 Future Research Directions -- Index.

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