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This paper describes an architecture for predicting the price ofcryptocurrencies for the next seven days using the Adaptive Network Based FuzzyInference System (ANFIS). Historical data of cryptocurrencies and indexes thatare considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D),and Ethereum Dominance (ETH.D) in a daily timeframe. The methods used to teachthe data are hybrid and backpropagation algorithms, as well as grid partition,subtractive clustering, and Fuzzy C-means clustering (FCM) algorithms, whichare used in data clustering. The architectural performance designed in thispaper has been compared with different inputs and neural network models interms of statistical evaluation criteria. Finally, the proposed method canpredict the price of digital currencies in a short time.

international journal of computer science and information technology/international journal of computer science and information technology  (chennai. print)

An adaptive network-based approach for advanced forecasting of cryptocurrency values