Preface	7
	Acknowledgements	11
	Contents	12
	About the Authors	15
	1 An Introduction to Time-Series Prediction	17
	Abstract	17
	1.1 Defining Time-Series	17
	1.2 Importance of Time-Series Prediction	18
	1.3 Hindrances in Economic Time-Series Prediction	19
	1.4 Machine Learning Approach to Time-Series Prediction	20
	1.5 Scope of Machine Learning in Time-Series Prediction	22
	1.6 Sources of Uncertainty in a Time-Series	27
	1.7 Scope of Uncertainty Management by Fuzzy Sets	28
	1.8 Fuzzy Time-Series	31
	1.8.1 Partitioning of Fuzzy Time-Series	33
	1.8.2 Fuzzification of a Time-Series	35
	1.9 Time-Series Prediction Using Fuzzy Reasoning	38
	1.10 Single and Multi-Factored Time-Series Prediction	42
	1.11 Scope of the Book	44
	1.12 Summary	45
	References	48
	2 Self-adaptive Interval Type-2 Fuzzy Set Induced Stock Index Prediction	54
	Abstract	54
	2.1 Introduction	55
	2.2 Preliminaries	59
	2.3 Proposed Approach	60
	2.3.1 Training Phase	62
	2.3.1.1 Partitioning of Main Factor Close Prices into p Intervals of Equal Length	64
	2.3.1.2 Construction of IT2 or Type-1 Fuzzy Sets as Appropriate for Each Interval of Close Price	65
	2.3.1.3 Fuzzy Prediction Rule (FPR) Construction for Consecutive {\varvec c(t) } s	67
	2.3.1.4 Grouping of IT2/T1 Fuzzy Implications for Individual Main Factor Variation {\varvec V_{M}^{d} } (t)	68
	2.3.1.5 Computing Composite Secondary Variation Series (CSVS) and Its Partitioning	69
	2.3.1.6 Determining Secondary to Main Factor Variation Mapping	70
	2.3.2 Prediction Phase	71
	2.3.3 Prediction with Self-adaptive IT2/T1 MFs	74
	2.4 Experiments	75
	2.4.1 Experimental Platform	76
	2.4.2 Experimental Modality and Results	76
	2.4.2.1 Policies Adopted	76
	2.4.2.2 MF Selection	77
	2.4.2.3 Adaptation Cycle	77
	2.4.2.4 Varying d	80
	2.5 Performance Analysis	80
	2.6 Conclusion	82
	2.7 Exercises	83
	Appendix 2.1	90
	Appendix 2.2: Source Codes of the Programs	97
	References	115
	3 Handling Main and Secondary Factors in the Antecedent for Type-2 Fuzzy Stock Prediction	119
	Abstract	119
	3.1 Introduction	119
	3.2 Preliminaries	121
	3.3 Proposed Approach	123
	3.3.1 Method-I: Prediction Using Classical IT2FS	124
	3.3.2 Method-II: Secondary Factor Induced IT2 Approach	126
	3.3.3 Method-III: Prediction in Absence of Sufficient Data Points	128
	3.3.4 Method-IV: Adaptation of Membership Function in Method III to Handle Dynamic Behaviour of Time-Series [47–52]	134
	3.4 Experiments	135
	3.4.1 Experimental Platform	135
	3.4.2 Experimental Modality and Results	136
	3.5 Conclusion	141
	Appendix 3.1: Differential Evolution Algorithm [36, 48–50]	141
	References	143
	4 Learning Structures in an Economic Time-Series for Forecasting Applications	147
	Abstract	147
	4.1 Introduction	147
	4.2 Related Work	150
	4.3 DBSCAN Clustering—An Overview	151
	4.4 Slope-Sensitive Natural Segmentation	153
	4.4.1 Definitions	154
	4.4.2 The SSNS Algorithm	157
	4.5 Multi-level Clustering of Segmented Time-Blocks	159
	4.5.1 Pre-processing of Temporal Segments	159
	4.5.2 Principles of Multi-level DBSCAN Clustering	160
	4.5.3 The Multi-level DBSCAN Clustering Algorithm	162
	4.6 Knowledge Representation Using Dynamic Stochastic Automaton	163
	4.6.1 Construction of Dynamic Stochastic Automaton (DSA)	166
	4.6.2 Forecasting Using the Dynamic Stochastic Automaton	168
	4.7 Computational Complexity	170
	4.8 Prediction Experiments and Results	172
	4.9 Performance Analysis	173
	4.10 Conclusion	176
	Appendix 4.1: Source Codes of the Programs	177
	References	201
	5 Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-Induced Neural Regression	203
	Abstract	203
	5.1 Introduction	203
	5.2 Preliminaries	207
	5.2.1 Fuzzy Sets and Time-Series Partitioning	207
	5.2.2 Back-Propagation Algorithm	208
	5.2.3 Radial Basis Function (RBF) Networks	209
	5.3 First-Order Transition Rule Based NN Model	210
	5.4 Fuzzy Rule Based NN Model	215
	5.5 Experiments and Results	218
	5.5.1 Experiment 1: Sunspot Time-Series Prediction	218
	5.5.2 Experiment 2: TAIEX Close-Price Prediction	223
	5.6 Conclusion	227
	Appendix 5.1: Source Codes of the Programs	230
	References	246
	6 Conclusions	248
	6.1 Conclusions	248
	6.2 Future Research Directions	249
	Index	250