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Assessing the Predictability of Bitcoin Using AI and Statistical Models

Jegathees, Keshanth Jude, Usman, Aminu ORCID: https://orcid.org/0000-0002-4973-3585 and O'Dea, Mike ORCID: https://orcid.org/0000-0003-2112-0194 (2023) Assessing the Predictability of Bitcoin Using AI and Statistical Models. In: Maleh, Yassine, Alazab, Mamoun and Romdhani, Imed, (eds.) Blockchain for Cybersecurity in Cyber Physical Systems. Advances in Information Security (12). Springer

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Assessing the Predictability of Bitcoin using AI and Statistical Models.pdf - Accepted Version
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This chapter analyses Bitcoin's predictability using AI and statistical models, and then identifies the model that produces the most accurate results. A multivariate time series dataset was created in order to train an AI (LSTM) and statistical model (ARIMA) to predict the price of Bitcoin over time. The LSTM model achieved the highest accuracy of 94 percent and the lowest MAPE of 5%. ARIMA had the best overall metrics, but when it came to forecasting the future, it performed poorly. The results show that it is possible to predict the BTC with a reasonable error rate; however, Bitcoin is extremely volatile, making it difficult to obtain results that can be confidently used to assert its value ahead of time. The result of the study suggested that it is possible to forecast the Bitcoin price with minimal error rates, refuting the null hypothesis. However, because the bitcoin price index is affected by a variety of external sources, forecasting time series problems is intrinsically challenging. When attempting to predict stated sort of data, the following constraints must be considered: the models (1) do not account for exogenous variable uncertainty, and (2) do not account for the fact that forecast-error variances vary with time.

Item Type: Book Section
Status: Published
DOI: https://doi.org/10.1007/978-3-031-25506-9_11
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/6852

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