آمار
| تعداد نشریات | 45 |
| تعداد شمارهها | 1,477 |
| تعداد مقالات | 18,019 |
| تعداد مشاهده مقاله | 58,387,337 |
| تعداد دریافت فایل اصل مقاله | 19,805,922 |
Fuzzy Growing Map for Analyzing Cryptocurrency Market Trends | ||
| Computational Methods for Differential Equations | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 30 بهمن 1404 اصل مقاله (1.61 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2025.69796.3445 | ||
| نویسندگان | ||
| Pooria Poorakbari1؛ Samira Mohamadi2؛ Zahra Najafabadipour3؛ Majid Talebpoor3؛ Linda Manokians1؛ Hero Isavi* 4 | ||
| 1Department of Mangement and Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran. | ||
| 2Department of Doctorate of Business Administration, Santamonica Training Academy, Hermes elm Gostar Institute, Isfahan, Iran. | ||
| 3Department of Business Management, Faculty of Management, Islamic Azad University, Firoozkooh, Iran. | ||
| 4Department of Management, UR. C., Islamic Azad University, Urmia, Iran. | ||
| چکیده | ||
| The cryptocurrency market is renowned for its rapid fluctuations and high volatility, making trend prediction a significant challenge due to the complexity and noise in the data. Traditional predictive models often struggle with such unstructured and high-dimensional datasets. This study introduces the Fuzzy Growing Map (FGM) as a cutting-edge approach to analyze cryptocurrency market trends. FGM combines fuzzy logic with dynamic growing maps to uncover hidden patterns and correlations in the data, making it especially suitable for the unpredictable and fast-paced nature of the cryptocurrency domain. What sets FGM apart from conventional methods like neural networks, support vector machines, and regression models is its ability to dynamically adjust to new market changes in real-time}, without the need for time-consuming retraining. Unlike static models, FGM autonomously identifies key features from the input data and adapts continuously to emerging market conditions. When tested on historical cryptocurrency data, FGM demonstrated an impressive 92.3\% predictive accuracy, surpassing traditional models by 8-12\%. The results reveal that FGM is not only more adaptive and responsive to market shifts but also more efficient in predicting trends, offering a significant improvement over existing methods. This makes FGM a groundbreaking tool for the future of cryptocurrency market forecasting, capable of tackling the challenges posed by the volatility and noise inherent in these markets. | ||
| کلیدواژهها | ||
| Fuzzy Growing Map (FGM)؛ Cryptocurrency Market Prediction؛ Machine Learning؛ Trend Forecasting؛ Real-Time Adaptation | ||
|
آمار تعداد مشاهده مقاله: 13 تعداد دریافت فایل اصل مقاله: 23 |
||