A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of Timestamp Influence on Bitcoin Value

ALJOJO, Nahla, ALSHUTAYRI, Areej, ALDHAHRI, Eman, ALMANDEEL, Seita and ZAINOL, Azida (2021). A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of Timestamp Influence on Bitcoin Value. IEEE Access, 9, 148611-148624.

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Abstract

The transaction and market of bitcoin is volatile, meaning it's uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction of timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a "well-formulated" statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency's performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.

Item Type:	Article
Uncontrolled Keywords:	08 Information and Computing Sciences; 09 Engineering; 10 Technology; 40 Engineering; 46 Information and computing sciences
Identification Number:	https://doi.org/10.1109/access.2021.3124629
Page Range:	148611-148624
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	18 Apr 2024 12:11
Last Modified:	18 Apr 2024 12:11
URI:	https://shura.shu.ac.uk/id/eprint/33528

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