A Machine Learning Approach for Bitcoin Forecasting
This paper addresses the problem of directional forecasting for Bitcoin price movements. Methodologically, it proposes a lightweight, price-driven ensemble framework that integrates multi-dimensional OHLC time-series features; notably, it systematically validates— for the first time—that the low price (Low) exhibits the highest contribution to directional prediction, establishing it as the core feature. The framework combines a Gated Recurrent Unit (GRU) with benchmark models into an ensemble predictor and enhances interpretability via temporal feature importance analysis. Experimental results demonstrate that using only raw price sequences achieves state-of-the-art accuracy (>62%), while non-price features—including trading volume and sentiment—exhibit negligible impact. This work delineates the effective boundary of pure time-series price modeling and establishes a novel paradigm for cryptocurrency directional forecasting: concise, robust, and interpretable.