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.

本文研究了使用自适应网络模糊推理系统 (ANFIS) 预测未来七天加密货币价格的架构。使用每日时间框架的比特币 (BTC)、以太坊 (ETH)、比特币占主导地位 (BTC.D) 和以太坊占主导地位 (ETH.D) 的历史数据进行预测，采用混合和反向传播算法、网格划分、减法聚类和模糊 C 均值聚类 (FCM) 算法进行数据训练，通过统计评估准则将设计的架构性能与不同输入和神经网络模型进行比较。最终，该方法可以在短时间内预测数字货币价格。