Abstract
Nowadays, cryptocurrency has infiltrated almost all financial transactions; thus, it is generally recognized as an alternative method for paying and exchanging currency. Cryptocurrency trade constitutes a constantly increasing financial market and a promising type of profitable investment; however, it is characterized by high volatility and strong fluctuations of prices over time. Therefore, the development of an intelligent forecasting model is considered essential for portfolio optimization and decision making. The main contribution of this research is the combination of three of the most widely employed ensemble learning strategies: ensemble-averaging, bagging and stacking with advanced deep learning models for forecasting major cryptocurrency hourly prices. The proposed ensemble models were evaluated utilizing state-of-the-art deep learning models as component learners, which were comprised by combinations of long short-term memory (LSTM), Bi-directional LSTM and convolutional layers. The ensemble models were evaluated on prediction of the cryptocurrency price on the following hour (regression) and also on the prediction if the price on the following hour will increase or decrease with respect to the current price (classification). Additionally, the reliability of each forecasting model and the efficiency of its predictions is evaluated by examining for autocorrelation of the errors. Our detailed experimental analysis indicates that ensemble learning and deep learning can be efficiently beneficial to each other, for developing strong, stable, and reliable forecasting models.
Highlights
The global financial crisis of 2007–2009 was the most severe crisis over the last few decades with, according to the National Bureau of Economic Research, a peak to trough contraction of 18 months.The consequences were severe in most aspects of life including economy, social, leading in the long run to political instability and the need for further economic reforms
We aim to exploit the effectiveness of ensemble learning for reducing the bias or variance of error by exploiting multiple learners and the ability of deep learning models to learn the internal representation of the cryptocurrency data
We explored the adoption of ensemble learning strategies with advanced deep learning models for forecasting cryptocurrency price and movement, which constitutes the main contribution of this research
Summary
The global financial crisis of 2007–2009 was the most severe crisis over the last few decades with, according to the National Bureau of Economic Research, a peak to trough contraction of 18 months. The consequences were severe in most aspects of life including economy (investment, productivity, jobs, and real income), social (inequality, poverty, and social tensions), leading in the long run to political instability and the need for further economic reforms. In an attempt to “think outside the box”. Bypass the governments and financial institutions manipulation and control, Satoshi Nakamoto [1]. Proposed Bitcoin which is an electronic cash allowing online payments, where the double-spending problem was elegantly solved using a novel purely peer-to-peer decentralized blockchain along with a cryptographic hash function as a proof-of-work. There are over 5000 cryptocurrencies available; when it comes to scientific research there are several issues to deal with. In the Algorithms 2020, 13, 121; doi:10.3390/a13050121 www.mdpi.com/journal/algorithms
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