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Bitcoin Analysis and Forecasting through Fuzzy Transform

1
Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, 40126 Bologna, Italy
2
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; https://doi.org/10.3390/axioms9040139
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
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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.

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