Bitcoin price prediction jumped to 73% accuracy by simply looking at three timeframes at once, ignoring model complexity.
The quest for a 'better' financial AI usually involves deeper networks, but this paper shows the answer is simpler: multi-timeframe fusion. By integrating 15m, 1h, and 4h scales, prediction accuracy for Bitcoin direction skyrocketed from a coin-flip (~52%) to a highly profitable 73%. The breakthrough was scale integration, not model architecture. This is 'practical magic' for quantitative traders: it suggests that feature engineering across time is far more valuable than the latest transformer variant. It provides a blueprint for building more robust financial agents that don't get 'blinded' by single-scale noise. It's a clear win for data strategy over model hype.
Multi-Timeframe Fusion for Bitcoin Price Direction Prediction: An Empirical Study of Temporal Scale Integration
SSRN · 6580120
While experienced traders routinely analyze multiple timeframes, most deep learning approaches for cryptocurrency prediction operate on single temporal scales. This paper presents a systematic empirical study providing controlled quantification of discrete multi-timeframe fusion for Bitcoin price direction prediction. Through experiments on five years of BTC/USD data (2019–2024), we demonstrate that integrating multiple temporal scales yields a 20 percentage point accuracy improvement—from 51–53