用哈希率特征结合小波去噪，预测比特币未来1-90天价格与走势
Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach
- 通过小波变换去除噪声，融合深度学习与堆叠模型进行多时序预测
- 日度预测误差低至0.58%，90天预测准确率达82%
- 适合关注加密货币长期趋势的投资者与量化研究者
过去十年中，数字货币因其去中心化特性而广受欢迎，但其价格波动剧烈，催生了预测需求。作为最主流的比特币（BTC），成为研究热点。本文提出一种基于堆叠深度学习的分类与回归模型，利用小波变换降噪，结合哈希率等特征，实现对BTC未来1天、7天、30天和90天的价格变动与数值预测。预处理阶段采用卡方检验（Chi2）、递归特征消除（RFE）和嵌入式特征选择（Embedded）三种方法筛选特征。分类模型在预测未来1天、7天、30天和90天方向上分别达到63%、64%、67%和82%的准确率；价格预测方面，日度预测误差降至0.58%，7至90天预测误差范围为2.72%至2.85%。结果表明，该模型优于现有文献中的其他方法。
原文摘要 · Abstract (English)
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.
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