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An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
Department of Insurance and Statistics, University of Piraeus, GR 18-534 Piraeus, Greece
Department of Accounting and Finance, University of the Peloponnese, GR 241-00 Antikalamos, Greece
Author to whom correspondence should be addressed.
Academic Editor: Amir Mosavi
Electronics 2021, 10(3), 287;
Received: 16 December 2020 / Revised: 19 January 2021 / Accepted: 21 January 2021 / Published: 26 January 2021
(This article belongs to the Special Issue Regularization Techniques for Machine Learning and Their Applications)
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. View Full-Text
Keywords: deep learning; convolutional networks; LSTM; overfitting; time-series; forecasting deep learning; convolutional networks; LSTM; overfitting; time-series; forecasting
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MDPI and ACS Style

Livieris, I.E.; Kiriakidou, N.; Stavroyiannis, S.; Pintelas, P. An Advanced CNN-LSTM Model for Cryptocurrency Forecasting. Electronics 2021, 10, 287.

AMA Style

Livieris IE, Kiriakidou N, Stavroyiannis S, Pintelas P. An Advanced CNN-LSTM Model for Cryptocurrency Forecasting. Electronics. 2021; 10(3):287.

Chicago/Turabian Style

Livieris, Ioannis E., Niki Kiriakidou, Stavros Stavroyiannis, and Panagiotis Pintelas. 2021. "An Advanced CNN-LSTM Model for Cryptocurrency Forecasting" Electronics 10, no. 3: 287.

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