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Assessing the Predictability of Bitcoin Using AI and Statistical Models

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Blockchain for Cybersecurity in Cyber-Physical Systems

Part of the book series: Advances in Information Security ((ADIS,volume 102))

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Abstract

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 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% 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 various 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 (Fair, Chapter 33 Evaluating the predictive accuracy of models. In: Handbook of econometrics, vol 3, pp 1979–1995. Elsevier [Online]. Available at: https://doi.org/10.1016/S1573-4412(86)03013-1. Accessed 19 Jan 2022, 1986).

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Author information

Authors and Affiliations

  1. Department of Computer Science, York St John University, York, UK

    Keshanth Jude Jegathees, Aminu Bello Usman & Michael ODea

Authors
  1. Keshanth Jude Jegathees
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  2. Aminu Bello Usman
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  3. Michael ODea
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Corresponding author

Correspondence to Aminu Bello Usman .

Editor information

Editors and Affiliations

  1. Sultan Moulay Slimane University, Sultan Moulay, Morocco

    Yassine Maleh

  2. Charles Darwin University, Canberra, ACT, Australia

    Mamoun Alazab

  3. Edinburgh Napier University, Edinburgh, UK

    Imed Romdhani

Appendices

Appendices

1.1 Appendix A: Data Retrieval Sources

Bitcoin price index, and Blockchain data were obtained on 18 February 2021 from the Blockchain API (https://blockchain.com/). In addition to that, the other exogenous variables, such as Gold Price and Exchange Rates were extracted from Investing.com: (https://uk.investing.com/commodities/gold,https://uk.investing.com/currencies/usd-cny-historical-data, respectively). Search volume data were retrieved by accessing the Google Trends website (http://www.google.com/trends) on 22 February 2021 and the Wikipedia article traffic statistics site (https://www.wikishark.com/) on 2 February 2021.

All Bitcoin and Blockchain historical data were validated and cross-checked for accuracy by comparing data in popular sources like CoinDesk (https://www.coindesk.com/price/bitcoin/), and Yahoo Finance (https://finance.yahoo.com/).

1.2 Appendix B: Model Data Pre-processing

LSTM: When training a network with data with large range of values, as large input values can slow down the learning and sometimes can prevent the network from learning effectively. Therefore, the dataset was scaled using the MinMax Scaler available in the sklearn library. This process scales the dataset values to fit between 0 and 1.

ARIMA: Selecting the order of the model is crucial to build a good model. Firstly, the data was explored as discussed in the Results section, unit root tests were conducted to identify the best order of differencing, and in search of the best model, a grid search with different orders was iteratively fit. Finally, the best model was determined by plotting the residuals and comparing AIC scores.

1.3 Appendix C: ADF Test Scores

 

Differencing order

Column

No-diff

diff(0)

diff(1)

Close

0.868240

 

0.000000e+00

Open

0.858296

 

0.000000e+00

High

0.873539

 

0.000000e+00

Low

0.856106

 

0.000000e+00

Estimated-transaction-volume-usd

0.361389

 

3.769493e-21

n-transactions

0.077388

 

1.535323e-19

Hash-rate

0.899546

 

4.206086e-26

Difficulty

0.948766

 

1.827273e-21

Cost-per-transaction

0.577504

 

2.476612e-14

Gold price

0.885700

 

6.186569e-21

Output-volume

0.023501

0.023501

 

Trade-volume

0.080964

 

6.361866e-22

USD-CNY Price

0.693700

 

9.939304e-21

SVI

0.006120

0.006120

 

Wikiviews

0.016293

0.016293

 

1.4 Appendix D: Findings

The boxplot shows the closing prices of each month throughout all the years, the price gradually grew during the first 4 months and began to decline over the next 5 months, and finally beginning to rise again. It must also be noted there are plenty of outliers following May, this could be due to the high gains for BTC during 2017 and 2019 May, which accumulated for over 50%. This could be what is causing the model to predictions with higher MAE, the smaller size the of observations was not comprehensive for the model to identify the outliers (Fig. 17).

Fig. 17
A bar plot of bitcoin closing price between January and December with error bars. The bar is maximum in March with a value of approximately 5000 to 42000.

BTC Closing Price throughout the decade

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Jegathees, K.J., Usman, A.B., ODea, M. (2023). Assessing the Predictability of Bitcoin Using AI and Statistical Models. In: Maleh, Y., Alazab, M., Romdhani, I. (eds) Blockchain for Cybersecurity in Cyber-Physical Systems. Advances in Information Security, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-031-25506-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-25506-9_11

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  • Online ISBN: 978-3-031-25506-9

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