An Explainable AI Framework for Ethereum Price Forecasting: Preliminary Analysis

Abstract

Cryptocurrency markets are difficult to model due to high volatility and multi-scale dynamics. This study investigates the directional predictability of crypto asset prices across multiple forecast horizons using Support Vector Machines (SVM). A daily Ethereum dataset (2018-2025), comprising candlesticks, technical indicators, and sentiment data, is used to predict upward or downward price movements from one to thirty days ahead. Model interpretability is achieved through SHAP, a popular XAI methodology, which quantifies feature contributions across various horizons. Results show that short-term forecasts approach random performance, while accuracy rises steadily with horizon length, peaking near 70% around the 24-day horizon. SHAP analysis reveals that short horizons rely on fast-reacting momentum indicators, whereas longer horizons emphasize slower, trend-following features. These findings highlight that medium-term price movements contain more structured information and demonstrate how explainable machine learning can uncover horizon-dependent dynamics in digital asset markets.