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Dergiler / International Journal of Applied Mathematics Electronics and Computers / 2026 / Volume: 9 Issue: 3

Predicting COVID-19 impact on demand and supply of cryptocurrency using machine learning

David Oyewola(Federal University Kashere, Gombe State)0000-0001-9638-8764
Emmanuel Dada(University of Maiduguri)0000-0002-1132-5447
Juliana Ndunagu(National Open University of Nigeria)0000-0002-1313-1398
Daniel Eneojo Emmanuel(Federal University of Kashere)0000-0001-9198-2297
Sayfa
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Yayın Tarihi
2021-09-30
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Auto encoder was utilized for the labelling of the trading strategies buy-hold-sell.

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Amaç & Hipotez:In the wake of recent pandemic of COVID-19, we explore its unprecedented impact on the demand and supply of cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Decision Trees Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory and Noise Bagging (NBG).
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Temel Bulgular & Veri:The study employed Noise filters to enhance the performance of Decision Trees Bagging named NBG.
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Sonuç & Katkı:Auto encoder was utilized for the labelling of the trading strategies buy-hold-sell.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2018 Verileri
⚙️Değişkenler:Bitcoin · Cryptocurrency · Support vector machine

Abstract

In the wake of recent pandemic of COVID-19, we explore its unprecedented impact on the demand and supply of cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Decision Trees Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory and Noise Bagging (NBG). The study employed Noise filters to enhance the performance of Decision Trees Bagging named NBG. Dataset utilized for this analysis were obtained from the website of Coin Market Cap, including: Binance Coin (BCN), BitCoin Cash (BCH), BitCoin (BTC), BitCoinSV (BSV), Cardano (CDO), Chainlink (CLK), CryptoCoin (CCN), EOS (EOS), Ethereum (ETH), LiteCoin (LTC), Monero (MNO), Stellar (SLR), Tether (TTR), Tezos (TZS), XRP (XRP), and daily data collected from exchange markets platforms spans from 2nd January 2018 to 7th July 2020. Auto encoder was utilized for the labelling of the trading strategies buy-hold-sell.

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Bu Makaleye Atıf Yap

Oyewola, D., Dada, E., Ndunagu, J., Emmanuel, D. E. (2021). Predicting COVID-19 impact on demand and supply of cryptocurrency using machine learning. International Journal of Applied Mathematics Electronics and Computers, Volume: 9 Issue: 3. https://doi.org/10.18100/ijamec.958160

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