Neuro-Fuzzy Modeling Techniques in Economics

Neuro-Fuzzy Modeling Techniques in Economics

Fuzzy time series forecasting using semantic artificial intelligence tools

DOI:

10.33111/nfmte.2022.157

Анотація:
Abstract: This study investigates the application of Fuzzy Time Series (FTS) methods in forecasting the Bitcoin market. FTS methods have gained significant attention due to their simplicity, adaptability, forecasting precision, and computational efficiency. They generate interpretable representations of time series patterns, enabling knowledge transfer, auditability, reusability, and upgradability. The study specifically focuses on time-invariant rule-based FTS techniques, namely the conventional First-Order FTS (Song and Chen) and Weighted First-Order FTS (Yu) models. The research rigorously evaluates and compares the predictive performance of these methods across a range of accuracy metrics. Additionally, the article expands the understanding and application of FTS methods in cryptocurrency forecasting. Through comprehensive experimental evaluations and statistical analyses, it uncovers insights into the strengths, limitations, and potential areas for improvement of these FTS approaches. By highlighting their comparative accuracy and computational efficiency, the research contributes to the existing studies and provides practical recommendations for researchers and practitioners in the cryptocurrency domain.
Ключові слова:
Key words: fuzzy time series, fuzzy set, fuzzy logical relationships group, fuzzy time series forecasting, Bitcoin, cryptocurrency market
УДК:
UDC:

JEL: C53 C63 G10

To cite paper
In APA style
Bielinskyi, A., Soloviev, V., Solovieva, V., & Velykoivanenko, H. (2022). Fuzzy time series forecasting using semantic artificial intelligence tools. Neuro-Fuzzy Modeling Techniques in Economics, 11, 157-198. http://doi.org/10.33111/nfmte.2022.157
In MON style
Белінський А., Соловйов В., Соловйова В., Великоіваненко Г. Fuzzy time series forecasting using semantic artificial intelligence tools. Нейро-нечіткі технології моделювання в економіці. 2022. № 11. С. 157-198. http://doi.org/10.33111/nfmte.2022.157 (дата звернення: 19.06.2024).
With transliteration
Bielinskyi, A., Soloviev, V., Solovieva, V., Velykoivanenko, H. (2022) Fuzzy time series forecasting using semantic artificial intelligence tools. Neuro-Fuzzy Modeling Techniques in Economics, no. 11. pp. 157-198. http://doi.org/10.33111/nfmte.2022.157 (accessed 19 Jun 2024).
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