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

Record Type:	Electronic resources : Monograph/item
Title/Author:	High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data.
Author:	Zakiyeva, Nazgul.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2020
Description:	145 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
Subject:	Sparsity.
Online resource:	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:

915871
Sparsity.
Subjects--Index Terms:

Applied probability

High-Dimensional Time Series Modeling and Forecasting with Application to Energy Network Data.
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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.
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650 4 $a Optimization. $3 274084
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650 4 $a Gas flow. $3 199344
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