國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Time-series prediction and applicati...

Bhattacharya, Diptendu.

Time-series prediction and applicationsa machine intelligence approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Time-series prediction and applicationsby Amit Konar, Diptendu Bhattacharya.
其他題名:	a machine intelligence approach /
作者:	Konar, Amit.
其他作者:	Bhattacharya, Diptendu.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xviii, 242 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Time-series analysisData processing.
電子資源:	http://dx.doi.org/10.1007/978-3-319-54597-4
ISBN:	9783319545974$q(electronic bk.)

Time-series prediction and applicationsa machine intelligence approach /
Konar, Amit.

Time-series prediction and applicationsa machine intelligence approach /[electronic resource] :by Amit Konar, Diptendu Bhattacharya. - Cham :Springer International Publishing :2017. - xviii, 242 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1271868-4394 ;. - Intelligent systems reference library ;v.24..

An 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.

This 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.

ISBN: 9783319545974$q(electronic bk.)

Standard No.: 10.1007/978-3-319-54597-4doiSubjects--Topical Terms:

190935
Time-series analysis
--Data processing.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Time-series prediction and applicationsa machine intelligence approach /
LDR:02538nmm a2200325 a 4500 001 509168
003 DE-He213
005 20170914162548.0
006 m d
007 cr nn 008maaau
008 171121s2017 gw s 0 eng d
020 $a 9783319545974$q(electronic bk.)
020 $a 9783319545967$q(paper)
024 7 $a 10.1007/978-3-319-54597-4 $2 doi
035 $a 978-3-319-54597-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA280
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 519.55 $2 23
090 $a QA280 $b .K82 2017
100 1 $a Konar, Amit. $3 256291
245 1 0 $a Time-series prediction and applications $h [electronic resource] : $b a machine intelligence approach / $c by Amit Konar, Diptendu Bhattacharya.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xviii, 242 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.127
505 0 $a An 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 $a This 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 $a Time-series analysis $x Data processing. $3 190935
650 0 $a Machine learning. $3 188639
650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 274020
700 1 $a Bhattacharya, Diptendu. $3 775795
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.24. $3 558591
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-54597-4
950 $a Engineering (Springer-11647)