Fuzzy intelligent forecasting approaches and tools in the field of digital currencies: A systematic review

Digital currency, is one of the most important factors in the success of organizations that will be present in the arena of global competition. In the present review, the most important theories of digital currency forecasting based on fuzzy hybrid models and artificial neural networks have been systematically investigated. These models mainly focus on supervised methods for measuring hybrid models. Also, basic concepts about the history of hybrid models from the first proposed models to current developed models, their combinations and architectural capabilities, data processing and measurement methods of these intelligent models are presented so that evolution This category of intelligent systems is analyzed. Finally, the features of prominent (leading) models and their applications in digital currency forecasting are presented. The results show that fuzzy neural network models and their derivatives are efficient in predicting digital currency with very high accuracy and with good justification capability that is used in a wide range of economic and scientific fields.