語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
到查詢結果
[ subject:"Time Series Analysis." ]
切換:
標籤
|
MARC模式
|
ISBD
Data driven model learning for engin...
~
Mercere, Guillaume.
FindBook
Google Book
Amazon
博客來
Data driven model learning for engineers = with applications to univariate time series /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Data driven model learning for engineers/ by Guillaume Mercere.
其他題名:
with applications to univariate time series /
作者:
Mercere, Guillaume.
出版者:
Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:
x, 212 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Engineering - Mathematical models -
電子資源:
https://doi.org/10.1007/978-3-031-31636-4
ISBN:
9783031316364
Data driven model learning for engineers = with applications to univariate time series /
Mercere, Guillaume.
Data driven model learning for engineers
with applications to univariate time series /[electronic resource] :by Guillaume Mercere. - Cham :Springer Nature Switzerland :2023. - x, 212 p. :ill. (some col.), digital ;24 cm.
The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.
ISBN: 9783031316364
Standard No.: 10.1007/978-3-031-31636-4doiSubjects--Topical Terms:
3665350
Engineering
--Mathematical models
LC Class. No.: TA342
Dewey Class. No.: 620.00151955
Data driven model learning for engineers = with applications to univariate time series /
LDR
:02595nmm a2200313 a 4500
001
2334064
003
DE-He213
005
20230809171126.0
006
m d
007
cr nn 008maaau
008
240402s2023 sz s 0 eng d
020
$a
9783031316364
$q
(electronic bk.)
020
$a
9783031316357
$q
(paper)
024
7
$a
10.1007/978-3-031-31636-4
$2
doi
035
$a
978-3-031-31636-4
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
TA342
072
7
$a
PBT
$2
bicssc
072
7
$a
MAT029000
$2
bisacsh
072
7
$a
PBT
$2
thema
082
0 4
$a
620.00151955
$2
23
090
$a
TA342
$b
.M556 2023
100
1
$a
Mercere, Guillaume.
$3
3665349
245
1 0
$a
Data driven model learning for engineers
$h
[electronic resource] :
$b
with applications to univariate time series /
$c
by Guillaume Mercere.
260
$a
Cham :
$b
Springer Nature Switzerland :
$b
Imprint: Springer,
$c
2023.
300
$a
x, 212 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
520
$a
The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.
650
0
$a
Engineering
$x
Mathematical models
$x
Data processing.
$3
3665350
650
0
$a
Time-series analysis.
$3
532530
650
1 4
$a
Time Series Analysis.
$3
3538821
650
2 4
$a
Statistical Learning.
$3
3597795
650
2 4
$a
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
$3
3591853
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
856
4 0
$u
https://doi.org/10.1007/978-3-031-31636-4
950
$a
Mathematics and Statistics (SpringerNature-11649)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9460269
電子資源
11.線上閱覽_V
電子書
EB TA342
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入