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An Adaptive Network-Based Approach for Advanced Forecasting Of Crypto Currency Values


Affiliations
1 School of Electrical and Electronic Engineering, Newcastle University, Newcastle, United Kingdom
2 Department of Computer Science, Kent State University, Ohio, United States
 

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.

Keywords

Cryptocurrency price prediction; ANFIS; Semi-automatic system; Machine learning
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  • An Adaptive Network-Based Approach for Advanced Forecasting Of Crypto Currency Values

Abstract Views: 55  |  PDF Views: 28

Authors

Ali Mehrban
School of Electrical and Electronic Engineering, Newcastle University, Newcastle, United Kingdom
Pegah Ahadian
Department of Computer Science, Kent State University, Ohio, United States

Abstract


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.

Keywords


Cryptocurrency price prediction; ANFIS; Semi-automatic system; Machine learning

References