Forecasting Cryptocurrency Prices Using Ensembles-Based Machine Learning Approach

Derbentsev, Vasily; Datsenko, Natalia; Babenko, Vitalina; Pushko, Olha; Pursky, Oleg

doi:10.1109/picst51311.2020.9468090

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…Similarly, Farouk et al [5] also proposed that RF outperformed LR, AdaBoost, DT, KNN, GB, and neural networks in two of the datasets considered using R 2 , Mean Absolute Percentage Error (MAPE) and MAE as the performance metrics. Derbentsev et al [4] also confirmed that among the ensemble-based ML approaches, RF performed better than boosting in forecasting cryptocurrency prices. Both Bagging and GB reduce bias and enhance accuracy when dealing with complex relationships or imbalanced data.…”

Section: Resultsmentioning

confidence: 87%

“…The use of GB, RF, and Bagging regression in predicting the price of financial series has gained popularity, probably because these approaches show some robustness against overfitting compared to the use of conventional regression algorithms. Derbentsev et al [4] explored the use of these algorithms and found that RF regression performed better than other ensemble methods. Similarly, Farouk et al [5] compared the performance of RF and boosting regression with other algorithms in predicting the price of Bitcoin, and found that the RF regression performed better.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Samson

2024

IDA

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The decentralised nature of cryptocurrency, coupled with its potential for significant financial returns, has elevated its status as a sought-after investment opportunity on a global scale. Nonetheless, the inherent unpredictability and volatility of the cryptocurrency market present considerable challenges for investors aiming to forecast price movements and secure profitable investments. In response to this challenge, the current investigation was conducted to assess the efficacy of three Machine Learning (ML) algorithms, namely, Gradient Boosting (GB), Random Forest (RF), and Bagging, in predicting the daily closing prices of six major cryptocurrencies, namely,

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Section: Resultsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Samson

2024

IDA

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“…In addition to predicting the price of Bitcoin, there are many studies using LSTM to predict other digital currencies (Sebastião and Godinho 2021;Saadah and Whafa 2020;Derbentsev et al 2020). Politis et al (2021) used LSTM to predict the price of Ether with an accuracy of 84.2%.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Bitcoin Price Prediction Using Machine Learning

Chen

2023

JRFM

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The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy.

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“…Conforme pode-se ver pela análise das pesquisas da Tabela 1, a ampla maioria das pesquisas (AWOKE et al 2020, WU et al 2018, IQBAL et al 2021, INDULKAR 2021, DERBENTSEV et al 2020, LI and DAI 2020, FLEISCHER et al 2022) foca em predição da cotação de fechamento usando dados de periodicidade diária, fazendo a predição para o dia seguinte (horizonte 1). Uma exceção é a pesquisa de Jakubowicz and Abdelfattah (2021) A principal criptomoeada analisada nos estudos é a Bitcoin, que é seguida pela criptomoeda Ethereum.…”

Section: Trabalhos Correlatosunclassified

Predição da Cotação Real/Bitcoin usando a Rede Neural Long Short Term Memory

Leite¹,

Filho²,

Junior³

et al. 2023

Braz. J. Develop.

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Cryptocurrencies have become an important financial asset for com- panies, governments and civilians, being accepted for financial transactions in place of traditional currencies. However, cryptocurrencies, which are attractive for some reasons such as not having government control, have high volatility, making price predictions important in order to have a basis for deciding when to buy and when to sell this type of asset, in order to maximize profits. As pre- diction by people demands a specialist, which is expensive and also subject to biases and errors, several studies have investigated the application of artificial intelligence computational techniques for prediction. This study investigates the application of an LSTM neural network for predicting the price of the Bitcoin cryptocurrency in Reais, analyzing parameters of the neural network, together with the network modeling for the prediction of a single value for a given future time horizon and the prediction of multiple values for multiple future instants of time. The results obtained indicate that the single output model has a good performance for short-term loss, mainly in the prediction for the next day (horizon 1) with a mean absolute percentage error (MAPE) of 2.57% in its best configuration, however this error is not linearly scalable when the prediction horizon is increased. The multiple-output model produced higher MAPE values for short-term predictions (horizons 1, 3 and 5), however the error increase rate is lower than that of the simple horizon model.

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Forecasting Cryptocurrency Prices Using Ensembles-Based Machine Learning Approach

Cited by 12 publications

References 20 publications

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Analysis of Bitcoin Price Prediction Using Machine Learning

Predição da Cotação Real/Bitcoin usando a Rede Neural Long Short Term Memory

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