東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Decomposition-Based Ensemble Gaussia...

Wang, Hao.

Linked to FindBook

Google Book

Amazon

博客來

Decomposition-Based Ensemble Gaussian Process for Non-Stationary Time Series.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Decomposition-Based Ensemble Gaussian Process for Non-Stationary Time Series./
Author:	Wang, Hao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	58 p.
Notes:	Source: Masters Abstracts International, Volume: 81-03.
Contained By:	Masters Abstracts International81-03.
Subject:	Industrial engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13880600
ISBN:	9781085634601

Decomposition-Based Ensemble Gaussian Process for Non-Stationary Time Series.
Wang, Hao.

Decomposition-Based Ensemble Gaussian Process for Non-Stationary Time Series. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 58 p.

Source: Masters Abstracts International, Volume: 81-03.

Thesis (M.S.)--State University of New York at Binghamton, 2019.

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

Time series forecast has become an essential application in various fields. For example, it has demonstrated to be an efficient tool in stock price predicting, wind speed forecasting, and commodity pricing. Most of the developed models of Time Series Forecast do not have accurate prediction results regarding nonstationary and nonlinear time series. This thesis presents a time series model based on Gaussian process aiming to implement one-step prediction on nonstationary time series. Firstly, empirical mode decomposition is utilized to separate nonstationary time series into serval stationary components at a different frequency. Then, the Gaussian Process Regression in an autoregressive way is implemented on each component. The combination of all the components predication results improves the forecasting accuracy. Experiment with two groups of data from real-industry proves that the decomposition-based ensemble Gaussian Process model has better performance in terms of prediction accuracy than the traditional Gaussian Process Regression and autoregressive moving average model.

ISBN: 9781085634601Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Decomposition

Decomposition-Based Ensemble Gaussian Process for Non-Stationary Time Series.
LDR:02307nmm a2200385 4500 001 2268665
005 20200824072423.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781085634601
035 $a (MiAaPQ)AAI13880600
035 $a AAI13880600
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wang, Hao. $3 1911290
245 1 0 $a Decomposition-Based Ensemble Gaussian Process for Non-Stationary Time Series.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 58 p.
500 $a Source: Masters Abstracts International, Volume: 81-03.
500 $a Advisor: Cheng, Changqing.
502 $a Thesis (M.S.)--State University of New York at Binghamton, 2019.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Time series forecast has become an essential application in various fields. For example, it has demonstrated to be an efficient tool in stock price predicting, wind speed forecasting, and commodity pricing. Most of the developed models of Time Series Forecast do not have accurate prediction results regarding nonstationary and nonlinear time series. This thesis presents a time series model based on Gaussian process aiming to implement one-step prediction on nonstationary time series. Firstly, empirical mode decomposition is utilized to separate nonstationary time series into serval stationary components at a different frequency. Then, the Gaussian Process Regression in an autoregressive way is implemented on each component. The combination of all the components predication results improves the forecasting accuracy. Experiment with two groups of data from real-industry proves that the decomposition-based ensemble Gaussian Process model has better performance in terms of prediction accuracy than the traditional Gaussian Process Regression and autoregressive moving average model.
590 $a School code: 0792.
650 4 $a Industrial engineering. $3 526216
650 4 $a Applied mathematics. $3 2122814
650 4 $a Energy. $3 876794
650 4 $a Systems science. $3 3168411
650 4 $a Electrical engineering. $3 649834
653 $a Decomposition
653 $a Gaussian process regression
653 $a Time series
690 $a 0546
690 $a 0364
690 $a 0544
690 $a 0790
690 $a 0791
710 2 $a State University of New York at Binghamton. $b Systems Science Industrial Engineering. $3 2104041
773 0 $t Masters Abstracts International $g 81-03.
790 $a 0792
791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13880600