Fuzzy Mathematics and MicroTEDAClus in Time Series Analysis: Applications in Bitcoin Price Prediction.
Saved in:
| Title: | Fuzzy Mathematics and MicroTEDAClus in Time Series Analysis: Applications in Bitcoin Price Prediction. |
|---|---|
| Authors: | Laarichi, Yassine1 (AUTHOR) yassine.Laarichi@uit.ac.ma, Rholam, Oualid1 (AUTHOR), Aloui, Amal1 (AUTHOR), Elkaf, Mariem2 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. Nov2025, Vol. 49 Issue 5, p1150-1159. 10p. |
| Subjects: | Fuzzy mathematics, Time series analysis, Machine learning, Fuzzy clustering technique, Recurrent neural networks, Economic forecasting, Cluster analysis (Statistics), Economic decision making |
| Abstract: | Time series forecasting presents significant challenges for traditional models due to inherent noise, non-stationary patterns, and uncertainty. This study proposes a novel hybrid framework integrating fuzzy logic, dynamic clustering, and LSTM networks to address these challenges. While machine learning approaches like Long Short-Term Memory (LSTM) networks have shown promise, they often struggle with noise, uncertainty, and non-stationary patterns inherent in cryptocurrency markets. This study addresses these limitations by proposing a novel hybrid framework that integrates fuzzy logic, MicroTEDAClus dynamic clustering, and LSTM networks. The framework introduces three key innovations: fuzzy logic-based categorization to handle market uncertainty and linguistic ambiguity, MicroTEDAClus clustering to adaptively segment data streams and capture evolving price regimes, and an LSTM architecture optimized for modelling temporal dependencies in clustered subsets. The methodology, applied to historical Bitcoin price data, underwent rigorous evaluation through preprocessing, dynamic clustering, and multi-phase model training. The experimental results demonstrate the framework's effectiveness, achieving a Train RMSE of 2687.93, a Test RMSE of 4872.49, a Train MAPE of 2.73%, and a Test MAPE of 4.40%. These findings highlight the potential of hybrid models for improved financial decision-making in volatile markets. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Time series forecasting presents significant challenges for traditional models due to inherent noise, non-stationary patterns, and uncertainty. This study proposes a novel hybrid framework integrating fuzzy logic, dynamic clustering, and LSTM networks to address these challenges. While machine learning approaches like Long Short-Term Memory (LSTM) networks have shown promise, they often struggle with noise, uncertainty, and non-stationary patterns inherent in cryptocurrency markets. This study addresses these limitations by proposing a novel hybrid framework that integrates fuzzy logic, MicroTEDAClus dynamic clustering, and LSTM networks. The framework introduces three key innovations: fuzzy logic-based categorization to handle market uncertainty and linguistic ambiguity, MicroTEDAClus clustering to adaptively segment data streams and capture evolving price regimes, and an LSTM architecture optimized for modelling temporal dependencies in clustered subsets. The methodology, applied to historical Bitcoin price data, underwent rigorous evaluation through preprocessing, dynamic clustering, and multi-phase model training. The experimental results demonstrate the framework's effectiveness, achieving a Train RMSE of 2687.93, a Test RMSE of 4872.49, a Train MAPE of 2.73%, and a Test MAPE of 4.40%. These findings highlight the potential of hybrid models for improved financial decision-making in volatile markets. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 10641246 |
| DOI: | 10.1177/18758967251336504 |
