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020 _a9783319545974
_9978-3-319-54597-4
024 7 _a10.1007/978-3-319-54597-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aKonar, Amit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957590
245 1 0 _aTime-Series Prediction and Applications
_h[electronic resource] :
_bA Machine Intelligence Approach /
_cby Amit Konar, Diptendu Bhattacharya.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVIII, 242 p. 69 illus., 13 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v127
505 0 _aAn Introduction to Time-Series Prediction -- Prediction Using Self-Adaptive Interval Type-2 Fuzzy Sets -- Handling Multiple Factors in the Antecedent of Type-2 Fuzzy Rules -- Learning Structures in an Economic Time-Series for Forecasting Applications -- Grouping of First-Order Transition Rules for Time-Series Prediction by Fuzzy-induced Neural Regression -- Conclusions and Future Directions. .
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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aMathematics—Data processing.
_931594
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputational Mathematics and Numerical Analysis.
_931598
700 1 _aBhattacharya, Diptendu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957591
710 2 _aSpringerLink (Online service)
_957592
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319545967
776 0 8 _iPrinted edition:
_z9783319545981
776 0 8 _iPrinted edition:
_z9783319854359
830 0 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v127
_957593
856 4 0 _uhttps://doi.org/10.1007/978-3-319-54597-4
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79976
_d79976