Abstract
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.
Highlights
Cryptocurrencies have been established and widely recognized as a new electronic alternative exchange currency method, which have considerable implications for emerging economies and in general for the global economy [1]. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising type of profitable investments. This constantly increasing financial market is characterized by significant volatility and strong price fluctuations over time
Convolutional layers are utilized to filter out the noise in complex time-series data as well as extracting new valuable features while long short-term memory (LSTM) layers are used to efficiently capture sequence patterns as well as long and short term dependencies [9]
It is worth noticing that all deep learning models were trained using the transformed series and the inverse transformations were applied for calculating the prediction for the levels of the original time-series
Summary
Cryptocurrencies have been established and widely recognized as a new electronic alternative exchange currency method, which have considerable implications for emerging economies and in general for the global economy [1]. To impose stationarity the authors performed a series of transformations based on first differences or returns, without the loss of any embedded information They performed extensive research focusing on the evaluation of the prediction accuracy of deep learning models, as well as the reliability of their forecasts by examining the existence of autocorrelation in the errors. The rationale for the utilization of a multi-input neural network is that these types of models have been originally proposed for more efficiently exploiting mixed data and refers to the case of having multiple types of independent data [14] In the literature, these models have been successfully applied for addressing a variety of difficult real-world problems reporting promising results while they were found to outperform traditional single output models [14,15,16,17,18].
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