000 | 03647nam a22005535i 4500 | ||
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001 | 978-3-319-54597-4 | ||
003 | DE-He213 | ||
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007 | cr nn 008mamaa | ||
008 | 170325s2017 sz | s |||| 0|eng d | ||
020 |
_a9783319545974 _9978-3-319-54597-4 |
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024 | 7 |
_a10.1007/978-3-319-54597-4 _2doi |
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_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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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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 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aMathematics—Data processing. _931594 |
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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 |
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