Paper: arXiv 2401.05441

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced adaptive network-based fuzzy inference systems (ANFIS) with hybrid learning and clustering algorithms, indicating high mathematical density, while the empirical rigor is moderate due to the use of statistical evaluation criteria (RMSE, MAE) on historical cryptocurrency data, though it lacks explicit backtesting or live trading implementation details.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Predict Cryptocurrency Prices<br>for Next 7 Days"] --> B["Input Data:<br>BTC, ETH, BTC.D, ETH.D<br>(Daily Timeframe)"]
  B --> C["Data Clustering:<br>Grid Partition /<br>Subtractive / FCM"]
  C --> D["Model Architecture:<br>ANFIS<br>Adaptive Neuro-Fuzzy<br>Inference System"]
  D --> E["Training Algorithms:<br>Hybrid &<br>Backpropagation"]
  E --> F["Evaluation:<br>Comparison with<br>Neural Networks & Inputs"]
  F --> G["Outcome:<br>Accurate Short-Term<br>Price Forecasting"]