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

TL;DR

This paper introduces a deep stacking and wavelet-based model for Bitcoin price prediction, achieving high accuracy over multiple time horizons and outperforming existing methods.

Contribution

It presents a novel combination of wavelet noise removal, deep stacking, and feature selection for improved Bitcoin price forecasting.

Findings

Achieved up to 82% accuracy in 90-day predictions

Reduced daily price error to 0.58%

Outperformed existing models in literature

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.