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008			170329s2017 sz ob 001 0 eng d
020			_a3319545965
020			_a3319545973 _q(electronic bk.)
020			_a9783319545967
020			_a9783319545974 _q(electronic bk.)
020			_z9783319545967
040			_aN$T _cN$T _dEBLCP _dGW5XE _dN$T _dYDX _dOCLCF _dAZU _dUPM _dUAB _dESU _dIOG _dSTF _dJBG _dIAD _dICW _dICN _dILO _dVT2 _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA280 _b.K663 2017 EB
100	1		_aKonar, Amit, _eautor
245	1	0	_aTime-series prediction and applications : _ba machine intelligence approach _cAmit Konar, Diptendu Bhattacharya.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aIntelligent systems reference library _x1868-4394 _vvolume 127
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
505	0		_aPreface; 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.
505	8		_a1.10 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.
505	8		_a2.3.1.4 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.
505	8		_a3.5 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.
505	8		_aAppendix 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.
520	3		_aThis 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.
650		7	_aAprendizaje automático _2embne _0(OCoLC)fst01004795 _0 _9166090
700	1		_aBhattacharya, Diptendu, _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-54597-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95675 _d95675 _x1