Prediction of Cryptocurrency Price Changes Using a Custom Physics-Informed Neural Network

The volatility of cryptocurrency prices presents significant challenges for accurate prediction. This paper introduces a novel machine learning framework using a custom Physics-Informed Neural Network (PINN) to predict the closing prices of cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH). The proposed PINN model is benchmarked against optimized versions of Random Forest and k-Nearest Neighbor (KNN) models. The dataset, spanning five years, undergoes comprehensive pre-processing, including stationarity testing using the Augmented Dickey-Fuller (ADF) method, detrending through differencing transformation, and Min-Max normalization. Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are employed to assess model accuracy. Results indicate that the custom PINN outperforms traditional models, achieving RMSE values of 0.20201 for BTC and 0.17561 for ETH, representing improvements of 22.70% and 21.59% over KNN and Random Forest for BTC, and 27.27% and 25.37% for ETH, respectively. The study underscores the potential of incorporating physics-informed approaches into machine learning models to enhance the predictability of highly volatile financial markets.