@conference {2025,
	title = {Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction},
	year = {2025},
	abstract = {Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining{\textquoteright}s evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms LSTM-based and TSLANet baselines, achieving 83.7\% accuracy and 83.1\% macro F1-score. The model demonstrates strong economic relevance, achieving 93.6\% precision in detecting unprofitable periods and 98.5\% precision for profitable ones, while avoiding misclassification of profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations. The model is available at https://github.com/AMAAI-Lab/MineROI-Net},
	url = {https://arxiv.org/abs/2512.05402},
	author = {S. Wickramasinghe and B. Das and D. Herremans}
}
