Next Article in Journal
Incremental FPT Delay
Previous Article in Journal
A Novel Data-Driven Magnetic Resonance Spectroscopy Signal Analysis Framework to Quantify Metabolite Concentration
Previous Article in Special Issue
Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content
Open AccessArticle

Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series

Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
Department of Accounting & Finance, University of the Peloponnese, GR 241-00 Antikalamos, Greece
Author to whom correspondence should be addressed.
Algorithms 2020, 13(5), 121;
Received: 17 April 2020 / Revised: 7 May 2020 / Accepted: 8 May 2020 / Published: 10 May 2020
(This article belongs to the Special Issue Ensemble Algorithms and Their Applications)
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. View Full-Text
Keywords: deep learning; ensemble learning; convolutional networks; long short-term memory; cryptocurrency; time-series deep learning; ensemble learning; convolutional networks; long short-term memory; cryptocurrency; time-series
Show Figures

Figure 1

MDPI and ACS Style

Livieris, I.E.; Pintelas, E.; Stavroyiannis, S.; Pintelas, P. Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series. Algorithms 2020, 13, 121.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop