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Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction

Sithumi Wickramasinghe, Bikramjit Das, Dorien Herremans

TL;DR

This paper tackles the problem of when to acquire Bitcoin mining hardware by reframing it as a three-class time series classification task predicting one-year ROI categories. It introduces MineROI-Net, a Transformer-based architecture featuring a FFT-based spectral feature extractor and a channel-mixing module, designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024, MineROI-Net achieves 83.7% accuracy and 83.1% macro F1, with exceptionally high precision for unprofitable (93.6%) and profitable (98.5%) periods, across diverse market regimes. The results demonstrate the model's practical utility for data-driven, risk-conscious hardware acquisition timing and are complemented by open-source availability of the MineROI-Net implementation.

Abstract

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 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 through: https://github.com/AMAAI-Lab/MineROI-Net.

Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction

TL;DR

This paper tackles the problem of when to acquire Bitcoin mining hardware by reframing it as a three-class time series classification task predicting one-year ROI categories. It introduces MineROI-Net, a Transformer-based architecture featuring a FFT-based spectral feature extractor and a channel-mixing module, designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024, MineROI-Net achieves 83.7% accuracy and 83.1% macro F1, with exceptionally high precision for unprofitable (93.6%) and profitable (98.5%) periods, across diverse market regimes. The results demonstrate the model's practical utility for data-driven, risk-conscious hardware acquisition timing and are complemented by open-source availability of the MineROI-Net implementation.

Abstract

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 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 through: https://github.com/AMAAI-Lab/MineROI-Net.

Paper Structure

This paper contains 19 sections, 6 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Approach and model
  4. ROI calculation and class labeling
  5. Proposed model architecture
  6. Spectral Feature Extractor
  7. Channel Mixing Module
  8. Transformer Encoder
  9. Classification and Training.
  10. Experimental setup
  11. Dataset
  12. Baseline models.
  13. Rolling cross-validation strategy and hyperparameter search
  14. Results
  15. Performance across market regimes
  16. ...and 4 more sections

Figures (3)

  • Figure 1: MineROI-Net architecture overview.
  • Figure 2: Expanding window cross-validation strategy across market regimes.
  • Figure 3: Confusion matrices for MineROI-Net.