Using a generalised method of moment approach and 2D‐generalised autoregressive conditional heteroscedasticity modelling for denoising ultrasound images

Raslain, Safia; Hachouf, Fella; Kharfouchi, Soumia

doi:10.1049/iet-ipr.2018.5528

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Because the conditional heteroscedasticity model is the fitting of data, there is often a lack of explanation for the macro data of the model. [18][19][20] Therefore, the disturbance term of neural network can be added to the variance prediction equation to improve the fitting of conditional heteroscedasticity model to parities data. in this article, the conditional heteroscedasticity model is used to predict the parities as the input training network forecast RMB exchange rate.…”

Section: Nn Networkmentioning

confidence: 99%

RETRACTED: Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

Yin

2021

International Journal of Electrical Engineering & Education

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Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

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Section: Nn Networkmentioning

confidence: 99%