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