Forecasting of Cryptocurrency Prices Using Machine Learning

Derbentsev, Vasily; Matviychuk, Andriy; Soloviev, Vladimir

doi:10.1007/978-981-15-4498-9_12

Cited by 38 publications

(23 citation statements)

References 17 publications

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“…Derbentsev et al [3] attempted to model short-term dynamics of the three most capitalized cryptocurrencies, i.e., Bitcoin, Etherium, and Ripple, using several sophisticated prediction models. More specifically, they evaluated the prognostic performance of an artificial neural network (ANN), a random forest (RF), and a binary autoregressive tree (BART) model.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over time. Nowadays, cryptocurrency forecasting is generally considered as one of the most challenging time-series prediction problems due to the large number of unpredictable factors involved and the significant volatility of cryptocurrencies' prices, resulting in complicated temporal dependencies [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

et al. 2021

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Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

et al. 2021

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“…In recent times, algorithms of Machine Learning (ML) which have developed within data science paradigm [ 10 ] has been dominated. It has been applied to forecasting financial and economic time series [ 11 , 12 ]. Results of numerous empirical studies have shown that ML approaches outperform time series models in forecasting different financial assets [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

et al. 2022

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Background Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. Methods In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. Results An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. Conclusions Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

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“…Hence, the cryptocurrency prices exhibit such complex volatility characteristics as nonlinearity and uncertainty, which are difficult to forecast, and any obtained results are uncertain. Therefore, cryptocurrency price prediction remains a huge challenge [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58].…”

Section: Introductionmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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Forecasting of Cryptocurrency Prices Using Machine Learning

Cited by 38 publications

References 17 publications

An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

Econophysics of cryptocurrency crashes: an overview

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