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Machine Learning Algorithm for Cryptocurrencies Price Prediction

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Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 972))

Abstract

Cryptocurrency has grown outstandingly in recent years. Additional events throughout the planet have acknowledged the significance of embracing numeral benefits virtually with rapid advances seen in these directions. In today's financial market, the decision to buy or sell cryptocurrency is an interesting challenge faced by day traders. Over the year, it has reached unprecedented highs leading to thoughts explaining the trend in its growth. The idea of whether the movement of financial assets can be predicted has kept investors, economists, and researchers very engaged in recent years. Therefore, the paper used machine learning to construct a model for the Stock and Cryptocurrency price prediction using technical indicators that are most important for market trend study. This study learns how to adapt Long Short-Term Memory (LSTM) to build the cryptocurrency price prediction model. The key factors used are available price, close price, high price, low price, volume and market cap with the interdependencies amid some cryptocurrencies thus centers on measuring vital features that influence the trade’s unpredictability by applying the model to increase the effectiveness of the process. Nonetheless, the cryptocurrency market lacks firm regulatory structures and is unpredictable, making forecasting prices more difficult and complex. From the analysis, it was established that machine learning models provide better performance in predicting cryptocurrency price. The LSTM model outperformed other models in terms of Bitcoin, Ether and Litecoin cryptocurrencies. The proposed model is found to be efficient for cryptocurrency price prediction when compared to similar models with 67.43% accuracy.

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Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde & Rasheed Gbenga Jimoh

  2. Department of Computer Science, Landmark University, Omu Aran, Kwara, Nigeria

    Roseline Oluwaseun Ogundokun

  3. Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Department of Mathematical and Computing Sciences, KolaDaisi University, Omu-Aran, Nigeria

    Taye Oladele Aro

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  1. Joseph Bamidele Awotunde
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Correspondence to Roseline Oluwaseun Ogundokun .

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    Sanjay Misra

  2. Vellore Institute of Technology, Chennai, Tamil Nadu, India

    Amit Kumar Tyagi

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Awotunde, J.B., Ogundokun, R.O., Jimoh, R.G., Misra, S., Aro, T.O. (2021). Machine Learning Algorithm for Cryptocurrencies Price Prediction. In: Misra, S., Kumar Tyagi, A. (eds) Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities. Studies in Computational Intelligence, vol 972. Springer, Cham. https://doi.org/10.1007/978-3-030-72236-4_17

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