000780454 000__ 05909cam\a2200553Ii\4500
000780454 001__ 780454
000780454 005__ 20230306143011.0
000780454 006__ m\\\\\o\\d\\\\\\\\
000780454 007__ cr\nn\nnnunnun
000780454 008__ 170329s2017\\\\sz\\\\\\ob\\\\001\0\eng\d
000780454 019__ $$a984869258
000780454 020__ $$a9783319545974$$q(electronic book)
000780454 020__ $$a3319545973$$q(electronic book)
000780454 020__ $$z9783319545967
000780454 0247_ $$a10.1007/978-3-319-54597-4$$2doi
000780454 035__ $$aSP(OCoLC)ocn979992080
000780454 035__ $$aSP(OCoLC)979992080$$z(OCoLC)984869258
000780454 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dEBLCP$$dGW5XE$$dN$T$$dYDX$$dOCLCF$$dAZU$$dUPM$$dUAB
000780454 049__ $$aISEA
000780454 050_4 $$aQA280
000780454 08204 $$a519.5/5$$223
000780454 08204 $$a620
000780454 1001_ $$aKonar, Amit,$$eauthor.
000780454 24510 $$aTime-series prediction and applications :$$ba machine intelligence approach /$$cAmit Konar, Diptendu Bhattacharya.
000780454 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017.
000780454 300__ $$a1 online resource.
000780454 336__ $$atext$$btxt$$2rdacontent
000780454 337__ $$acomputer$$bc$$2rdamedia
000780454 338__ $$aonline resource$$bcr$$2rdacarrier
000780454 347__ $$atext file$$bPDF$$2rda
000780454 4901_ $$aIntelligent systems reference library,$$x1868-4394 ;$$vvolume 127
000780454 504__ $$aIncludes bibliographical references and index.
000780454 5050_ $$aPreface; Acknowledgements; Contents; About the Authors; 1 An Introduction to Time-Series Prediction; Abstract; 1.1 Defining Time-Series; 1.2 Importance of Time-Series Prediction; 1.3 Hindrances in Economic Time-Series Prediction; 1.4 Machine Learning Approach to Time-Series Prediction; 1.5 Scope of Machine Learning in Time-Series Prediction; 1.6 Sources of Uncertainty in a Time-Series; 1.7 Scope of Uncertainty Management by Fuzzy Sets; 1.8 Fuzzy Time-Series; 1.8.1 Partitioning of Fuzzy Time-Series; 1.8.2 Fuzzification of a Time-Series; 1.9 Time-Series Prediction Using Fuzzy Reasoning
000780454 5058_ $$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
000780454 5058_ $$a2.3.1.4 Grouping of IT2/T1 Fuzzy Implications for Individual Main Factor Variation {\varvec V_{M}^{d} } (t)2.3.1.5 Computing Composite Secondary Variation Series (CSVS) and Its Partitioning; 2.3.1.6 Determining Secondary to Main Factor Variation Mapping; 2.3.2 Prediction Phase; 2.3.3 Prediction with Self-adaptive IT2/T1 MFs; 2.4 Experiments; 2.4.1 Experimental Platform; 2.4.2 Experimental Modality and Results; 2.4.2.1 Policies Adopted; 2.4.2.2 MF Selection; 2.4.2.3 Adaptation Cycle; 2.4.2.4 Varying d; 2.5 Performance Analysis; 2.6 Conclusion; 2.7 Exercises; Appendix 2.1
000780454 5058_ $$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
000780454 5058_ $$a3.5 ConclusionAppendix 3.1: Differential Evolution Algorithm [36, 48-50]; References; 4 Learning Structures in an Economic Time-Series for Forecasting Applications; Abstract; 4.1 Introduction; 4.2 Related Work; 4.3 DBSCAN Clustering-An Overview; 4.4 Slope-Sensitive Natural Segmentation; 4.4.1 Definitions; 4.4.2 The SSNS Algorithm; 4.5 Multi-level Clustering of Segmented Time-Blocks; 4.5.1 Pre-processing of Temporal Segments; 4.5.2 Principles of Multi-level DBSCAN Clustering; 4.5.3 The Multi-level DBSCAN Clustering Algorithm; 4.6 Knowledge Representation Using Dynamic Stochastic Automaton
000780454 506__ $$aAccess limited to authorized users.
000780454 520__ $$aThis book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers’ ability and understanding of the topics covered.
000780454 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 4, 2017).
000780454 650_0 $$aTime-series analysis$$xData processing.
000780454 650_0 $$aMachine learning.
000780454 7001_ $$aBhattacharya, Diptendu,$$eauthor.
000780454 77608 $$iPrint version:$$z9783319545967
000780454 830_0 $$aIntelligent systems reference library ;$$vv. 127.
000780454 852__ $$bebk
000780454 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-54597-4$$zOnline Access$$91397441.1
000780454 909CO $$ooai:library.usi.edu:780454$$pGLOBAL_SET
000780454 980__ $$aEBOOK
000780454 980__ $$aBIB
000780454 982__ $$aEbook
000780454 983__ $$aOnline
000780454 994__ $$a92$$bISE