Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining'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 recurrent, convolutional, and attention-based baselines, achieving 83.2% accuracy and 83.5% macro F1-score. The model demonstrates strong economic relevance, achieving 97.8% precision in detecting unprofitable periods and 81.5% precision in detecting profitable ones, while avoiding misclassifying 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.

翻译：比特币挖矿硬件的购置需要战略性的时机把握，这源于市场波动剧烈、技术快速过时以及协议驱动的收益周期。尽管挖矿已演变为资本密集型行业，但关于何时购买新的专用集成电路（ASIC）硬件缺乏指导，且此前没有计算框架解决这一决策问题。我们通过将硬件购置问题表述为时间序列分类任务来填补这一空白，预测购买ASIC机器在一年内能否带来盈利（投资回报率（ROI）>= 1）、微利（0 < ROI < 1）或亏损（ROI <= 0）的回报。我们提出了MineROI-Net——一种开源、基于Transformer的架构，旨在捕获挖矿盈利能力中的多尺度时间模式。在2015年至2024年间发布的20款ASIC矿机数据上，跨越不同市场环境进行评估，MineROI-Net优于循环、卷积和基于注意力的基线模型，实现了83.2%的准确率和83.5%的宏F1分数。该模型展现出显著的经济相关性，在检测亏损时期达到97.8%的精确率，在检测盈利时期达到81.5%的精确率，同时避免了将盈利场景误判为亏损，反之亦然。这些结果表明，MineROI-Net为把握挖矿硬件购置时机提供了一种实用的数据驱动工具，可能有助于降低资本密集型挖矿运营中的财务风险。