東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Applied time series analysis and for...

Huang, Changquan.

FindBook

Google Book

Amazon

博客來

Applied time series analysis and forecasting with Python

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied time series analysis and forecasting with Python/ by Changquan Huang, Alla Petukhina.
作者:	Huang, Changquan.
其他作者:	Petukhina, Alla.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	x, 372 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Time-series analysis. -
電子資源:	https://doi.org/10.1007/978-3-031-13584-2
ISBN:	9783031135842

Applied time series analysis and forecasting with Python
Huang, Changquan.

Applied time series analysis and forecasting with Python[electronic resource] /by Changquan Huang, Alla Petukhina. - Cham :Springer International Publishing :2022. - x, 372 p. :ill. (chiefly color), digital ;24 cm. - Statistics and computing,2197-1706. - Statistics and computing..

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

ISBN: 9783031135842

Standard No.: 10.1007/978-3-031-13584-2doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: QA280 / .H83 2022

Dewey Class. No.: 519.55

Applied time series analysis and forecasting with Python
LDR:02208nmm a2200325 a 4500 001 2305067
003 DE-He213
005 20221019035628.0
006 m d
007 cr nn 008maaau
008 230409s2022 sz s 0 eng d
020 $a 9783031135842 $q (electronic bk.)
020 $a 9783031135835 $q (paper)
024 7 $a 10.1007/978-3-031-13584-2 $2 doi
035 $a 978-3-031-13584-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA280 $b .H83 2022
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.55 $2 23
090 $a QA280 $b .H874 2022
100 1 $a Huang, Changquan. $3 1252639
245 1 0 $a Applied time series analysis and forecasting with Python $h [electronic resource] / $c by Changquan Huang, Alla Petukhina.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a x, 372 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Statistics and computing, $x 2197-1706
520 $a This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
650 0 $a Time-series analysis. $3 532530
650 0 $a Time-series analysis $x Forecasting. $3 3607832
650 0 $a Time-series analysis $x Computer programs. $3 784594
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Time Series Analysis. $3 3538821
650 2 4 $a Statistical Software. $3 3596845
650 2 4 $a Econometrics. $3 542934
650 2 4 $a Python. $3 3201289
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
700 1 $a Petukhina, Alla. $3 3607831
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Statistics and computing. $3 1566755
856 4 0 $u https://doi.org/10.1007/978-3-031-13584-2
950 $a Mathematics and Statistics (SpringerNature-11649)