Next Article in Journal
A Self-Adaptive Shrinking Projection Method with an Inertial Technique for Split Common Null Point Problems in Banach Spaces
Previous Article in Journal
Singularly Perturbed Cauchy Problem for a Parabolic Equation with a Rational “Simple” Turning Point
Previous Article in Special Issue
On the Numerical Solution of Ordinary, Interval and Fuzzy Differential Equations by Use of F-Transform
Open AccessArticle

Bitcoin Analysis and Forecasting through Fuzzy Transform

Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, 40126 Bologna, Italy
Department of Economics, Society, Politics, University of Urbino Carlo Bo, 61029 Urbino, Italy
Author to whom correspondence should be addressed.
Axioms 2020, 9(4), 139;
Received: 9 September 2020 / Revised: 24 November 2020 / Accepted: 25 November 2020 / Published: 28 November 2020
(This article belongs to the Special Issue Fuzzy Transforms and Their Applications)
Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency. View Full-Text
Keywords: F-transform; Bitcoin; clustering; sentiment analysis F-transform; Bitcoin; clustering; sentiment analysis
Show Figures

Figure 1

MDPI and ACS Style

Guerra, M.L.; Sorini, L.; Stefanini, L. Bitcoin Analysis and Forecasting through Fuzzy Transform. Axioms 2020, 9, 139.

AMA Style

Guerra ML, Sorini L, Stefanini L. Bitcoin Analysis and Forecasting through Fuzzy Transform. Axioms. 2020; 9(4):139.

Chicago/Turabian Style

Guerra, Maria L.; Sorini, Laerte; Stefanini, Luciano. 2020. "Bitcoin Analysis and Forecasting through Fuzzy Transform" Axioms 9, no. 4: 139.

Find Other Styles
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