Show simple item record

AdvisorAbdel-Salam, Abdel-Salam G.
AuthorAL FAKIH, BATOUL MOHAMAD KAZEM
Available date2024-06-30T06:25:28Z
Publication Date2024-06
URIhttp://hdl.handle.net/10576/56261
AbstractThis research adopts a Gaussian prediction model for non-stationary time series. Then, we discuss four transformation techniques: Generalized Optimal Wavelet Decomposition Algorithm (GOWDA), Hilbert Huang transform (HHT), Detrending based on Echo State Networks (DESN), and Kolmogorov-Zurbenco (KZ) filter. GOWDA is an algorithm that runs the continuous wavelet transform (CWT) several times using different mother wavelet functions, maximal levels, and thresholding techniques. It chooses a combination with minimal error. Meanwhile, HHT combines echo state networks (ESNs), which decompose the time series into intrinsic mode functions (IMFs). Then, the Hilbert spectral analysis is applied to the IMFs before reconstructing the denoised signal. DESN is a neural network algorithm with minimal assumptions. KZ filter is a moving average algorithm that is easy to understand and implement. When comparing the performance of these methods with the Gaussian prediction model, it was found that the HHT reconstructed before prediction gave the best results.
Languageen
SubjectGaussian prediction model
QATAR STOCK MARKET
Generalized Optimal Wavelet Decomposition Algorithm (GOWDA)
Hilbert Huang transform (HHT)
Detrending based on Echo State Networks (DESN)
Kolmogorov-Zurbenco (KZ)
TitleON THE GAUSSIAN PROCESS FOR STATIONARY AND NON-STATIONARY TIME SERIES PREDICTION FOR THE QATAR STOCK MARKET
TypeMaster Thesis
DepartmentApplied Statistics
dc.accessType Full Text


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record