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Decomposition-Based Ensemble Gaussia...

Wang, Hao.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Decomposition-Based Ensemble Gaussian Process for Non-Stationary Time Series./
作者:	Wang, Hao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	58 p.
附註:	Source: Masters Abstracts International, Volume: 81-03.
Contained By:	Masters Abstracts International81-03.
標題:	Industrial engineering. -
電子資源:	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.
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