Hierarchical Intuitionistic TSK Fuzzy System for Bitcoin Price Forecasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920852" target="_blank" >RIV/00216275:25410/23:39920852 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10309793" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10309793</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/FUZZ52849.2023.10309793" target="_blank" >10.1109/FUZZ52849.2023.10309793</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Intuitionistic TSK Fuzzy System for Bitcoin Price Forecasting

Popis výsledku v původním jazyce

There has been great interest in developing hierarchical structures of fuzzy rule-based systems due to their flexibility allowing to model complex problems. To cope with the high degree of uncertainty arising from the characteristics of cryptocurrency markets, this paper proposes a hierarchical intuitionistic TSK (Takagi-Sugeno-Kang) fuzzy system equipped with a feature selection and feature ranking component. The proposed system uses intuitionistic fuzzy sets, allowing to effectively model investor uncertainty in the decision-making on cryptocurrency markets. The hierarchical structure is a parallel tree-like fuzzy system that is based on relevant features while considering feature dependencies. Computational efficiency is achieved by using fuzzy c-means clustering to produce rule antecedents. The proposed system is validated using multivariate bitcoin data for the period 2018 to 2022, showing that the proposed system can accurately predict bitcoin prices while retaining an interpretable hierarchical structure.

Název v anglickém jazyce

Hierarchical Intuitionistic TSK Fuzzy System for Bitcoin Price Forecasting

Popis výsledku anglicky

There has been great interest in developing hierarchical structures of fuzzy rule-based systems due to their flexibility allowing to model complex problems. To cope with the high degree of uncertainty arising from the characteristics of cryptocurrency markets, this paper proposes a hierarchical intuitionistic TSK (Takagi-Sugeno-Kang) fuzzy system equipped with a feature selection and feature ranking component. The proposed system uses intuitionistic fuzzy sets, allowing to effectively model investor uncertainty in the decision-making on cryptocurrency markets. The hierarchical structure is a parallel tree-like fuzzy system that is based on relevant features while considering feature dependencies. Computational efficiency is achieved by using fuzzy c-means clustering to produce rule antecedents. The proposed system is validated using multivariate bitcoin data for the period 2018 to 2022, showing that the proposed system can accurately predict bitcoin prices while retaining an interpretable hierarchical structure.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 IEEE International Conference on Fuzzy Systems (FUZZ) Proceedings

ISBN

979-8-3503-3228-5

ISSN

1098-7584

e-ISSN

—

Počet stran výsledku

6

Strana od-do

194453

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Inčchon

Datum konání akce

13. 8. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001103277400116