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.

翻译：本文描述了一种使用自适应神经模糊推理系统（ANFIS）预测未来七天加密货币价格的架构。所考虑的历史数据包括比特币（BTC）、以太坊（ETH）、比特币主导地位（BTC.D）和以太坊主导地位（ETH.D）的日线数据。数据训练方法采用混合算法和反向传播算法，数据聚类则应用了网格划分、减法聚类和模糊C均值聚类（FCM）算法。本文设计的架构性能已与不同输入及神经网络模型在统计评估标准方面进行了比较。最终，所提出的方法能够在短时间内预测数字货币价格。