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

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  • Ramin Mousa
  • Meysam Afrookhteh
  • Hooman Khaloo
  • Amir Ali Bengari
  • Gholamreza Heidary

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.

Suggested Citation

  • Ramin Mousa & Meysam Afrookhteh & Hooman Khaloo & Amir Ali Bengari & Gholamreza Heidary, 2025. "Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach," Papers 2501.13136, arXiv.org.
  • Handle: RePEc:arx:papers:2501.13136
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    References listed on IDEAS

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    3. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    4. Gençay, Ramazan & Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon J., 2001. "An Introduction to Wavelets and Other Filtering Methods in Finance and Economics," Elsevier Monographs, Elsevier, edition 1, number 9780122796708.
    5. Yianni Doumenis & Javad Izadi & Pradeep Dhamdhere & Epameinondas Katsikas & Dimitrios Koufopoulos, 2021. "A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets," Risks, MDPI, vol. 9(11), pages 1-15, November.
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