Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach

ArXiv ID: 2501.13136 “View on arXiv”

Authors: Unknown

Abstract

Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63% accuracy for predicting the next day and 64%, 67% and 82% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72% to 2.85% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.

Keywords: Bitcoin prediction, stack deep learning, wavelet denoising, feature selection, time-series forecasting, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning techniques (transformers, stacked models, wavelet denoising) but is heavily focused on data processing and real-world forecasting results with specific metrics, making it both mathematically complex and empirically rigorous.

  flowchart TD
    A["Research Goal: Predict Bitcoin<br>Prices & Movements"] --> B["Preprocessing<br>Wavelet Denoising & Feature Selection"]
    B --> C["Data: BTC Price &<br>Hashrate Features"]
    C --> D["Stack Deep Learning Model<br>Deep Learning + Transformers"]
    D --> E{"Forecast Horizons"}
    E --> F["1, 7, 30, 90 Days"]
    F --> G["Classification Accuracy:<br>63% to 82%"]
    F --> H["Regression Error:<br>0.58% to 2.85%"]