Comparative Performance of Machine Learning Ensemble Algorithms for Forecasting Cryptocurrency Prices

Derbentsev, Vasily; Babenko, Vitalina; Khrustalev, Kirill; Obruch, Hanna; Khrustalova, Sofiia

doi:10.5829/ije.2021.34.01a.16

Cited by 22 publications

(11 citation statements)

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“…Derbentsev et al [14] focused on predicting three cryptocurrencies using Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). The three cryptocurrencies are Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Closing Price of Cryptocurrency Ethereum

Ravele,

Sigauke,

Rambevha

2024

Preprint

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Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting Closing Price of Cryptocurrency Ethereum

Ravele,

Sigauke,

Rambevha

2024

Preprint

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“…al. [9], studied the short-term prediction problems of the time-series of cryptocurrencies (Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP)) by using a Supervised Machine Learning (ML) approach. They focused in their quest on the best methods of clustering as a stochastic gradient boosting machine, (SGBM) and random forests (RF).…”

Section: Literature Reviewmentioning

confidence: 99%

Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example)

Abdal-Hammed

Ghazal

Ibrahim

et al. 2022

jour

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redicting currency rates is important, for everyone who is trading and trying to build an investment portfolio from a range of crypto currencies. It is not subject to the same restrictions as fiat currencies. In this study, we seek to predict the exchange rate of BIT-COIN against the US dollar. The short-term data (365 observations) is processed using the LSTM model as one of the neural network models. Modeling is conducted by training a sample size of 67%, taking into account sharp fluctuations in the price of trade and a certain level of market efficiency. The GARCH model is used to select appropriate historical periods for how the LSTM model works and to test proficiency at the weak, semi-strong, and strong levels. The data series obtained from the website (Investing.com) have been processed. The researchers have found that the performance of the neural network improves as the EPOCH value increases with a training (research) period of 50 days before, which is consistent with the results of the proficiency test at the weak level. It agrees with the results of the sufficiency test at the weak level, which indicates that in the case under study (the Bitcoin market is effective at the weak level). It is advised that crypto-currency investors rely more on the historical trend of the price of the currency than on its current price, taking advantage of the artificial neural network model (LSTM) in dealing with little data of high volatility.

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“…The best cryptocurrency allocation was reported to be in the range from 5% to 20%, depending on the risk tolerance of the investor. The authors of [27] focus on time series data forecasting in particular and apply two machine learning algorithms, random forests (RF) and stochastic gradient boosting machine (SGBM). The results show that the ML ensemble technique can be used to anticipate Bitcoin values.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Hamayel

Owda

2021

136

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Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.

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Comparative Performance of Machine Learning Ensemble Algorithms for Forecasting Cryptocurrency Prices

Cited by 22 publications

References 0 publications

Predicting Closing Price of Cryptocurrency Ethereum

Predicting Closing Price of Cryptocurrency Ethereum

Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example)

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

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