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 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data./
作者:	Zakiyeva, Nazgul.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	145 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
標題:	Sparsity. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28832513
ISBN:	9798460442119

High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data.
Zakiyeva, Nazgul.

High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 145 p.

Source: Dissertations Abstracts International, Volume: 83-07, Section: B.

Thesis (Ph.D.)--National University of Singapore (Singapore), 2020.

This item must not be sold to any third party vendors.

The rise of "Big Data" over the past decades has provided access to a large amount of network time series data in various disciplines. We address the issues with high dimensionality and complex network dependence in a large-scale network time series by developing a novel Network Autoregressive model with demand and supply balance constraint (NAC). We assume sparsity and adopt a two-layer penalty in the estimation with an equality constraint. We also propose a nonlinear network autoregressive (NNAR) model to investigate the dynamics of complex network time series with high dimensionality and nonlinear spatial-temporal dependence. We conduct estimation using the profile least square method where the unknown link function is estimated with the local linear regression technique. We demonstrate the implementation of the proposed network models in forecasting gas demand and supply at 128 individual nodes in the German natural gas transmission network over a time frame of 22 months.

ISBN: 9798460442119Subjects--Topical Terms:

3680690
Sparsity.
Subjects--Index Terms:

Applied probability

High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data.
LDR:02224nmm a2200385 4500 001 2343922
005 20220513114353.5
008 241004s2020 ||||||||||||||||| ||eng d
020 $a 9798460442119
035 $a (MiAaPQ)AAI28832513
035 $a (MiAaPQ)USingapore182550
035 $a AAI28832513
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zakiyeva, Nazgul. $3 3682599
245 1 0 $a High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 145 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
502 $a Thesis (Ph.D.)--National University of Singapore (Singapore), 2020.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be sold to any third party vendors.
520 $a The rise of "Big Data" over the past decades has provided access to a large amount of network time series data in various disciplines. We address the issues with high dimensionality and complex network dependence in a large-scale network time series by developing a novel Network Autoregressive model with demand and supply balance constraint (NAC). We assume sparsity and adopt a two-layer penalty in the estimation with an equality constraint. We also propose a nonlinear network autoregressive (NNAR) model to investigate the dynamics of complex network time series with high dimensionality and nonlinear spatial-temporal dependence. We conduct estimation using the profile least square method where the unknown link function is estimated with the local linear regression technique. We demonstrate the implementation of the proposed network models in forecasting gas demand and supply at 128 individual nodes in the German natural gas transmission network over a time frame of 22 months.
590 $a School code: 1883.
650 4 $a Sparsity. $3 3680690
650 4 $a Seasonal variations. $3 3682600
650 4 $a Forecasting. $3 547120
650 4 $a Hilbert space. $3 558371
650 4 $a Optimization. $3 891104
650 4 $a Sociology. $3 516174
650 4 $a Natural gas distribution. $3 3682601
650 4 $a Energy. $3 876794
650 4 $a Euclidean space. $3 3562319
650 4 $a Time series. $3 3561811
650 4 $a Gas flow. $3 3487726
650 4 $a Ordinary differential equations. $3 3563004
650 4 $a Mathematics. $3 515831
650 4 $a Web studies. $3 2122754
650 4 $a Statistics. $3 517247
653 $a Applied probability
653 $a "Big Data"
690 $a 0791
690 $a 0626
690 $a 0338
690 $a 0405
690 $a 0646
690 $a 0463
710 2 $a National University of Singapore (Singapore). $3 3352228
773 0 $t Dissertations Abstracts International $g 83-07B.
790 $a 1883
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28832513