Prototyping an Explainable Artificial Intelligence Model (XLSM) for Forecasting the Bitcoin Price

Artificial Intelligence (AI) significantly improves time series forecasting in the financial market, yet it is challenging to establish reliable real-world finance applications due to a lack of transparency and explainability. This paper prototypes an eXplainable Long Short-Term Memory (XLSTM) model to train and forecast the price of Bitcoin using a group of 11 determinants. The results show good performance in price forecasting as measured by a mean absolute percentage error (MAPE) of 2.39% and an accuracy of 89.54%. Additionally, the AI model explains that trading volume and prices (Low, High, Open) contribute to the price dynamics, while oil and Dow Jones Index (DJI) influence the price behavior at a low level. We argue that understanding these underlying explanatory determinants may increase the reliability of AI’s prediction in the cryptocurrency and general finance market.