用组合融合分析提升比特币价格预测准确率
Bitcoin Price Prediction using Machine Learning and Combinatorial Fusion Analysis
- 融合多个模型的评分与排序特征，动态加权组合提升预测能力
- 实现0.19%的MAPE误差，显著优于单模型和现有方法
- 适合对金融预测精度要求高的研究者与量化交易从业者
本文提出一种名为组合融合分析（CFA）的新模型融合与学习范式，用于比特币价格预测。金融产品价格预测长期是金融领域的热点，成功预测可带来显著收益。各类机器学习模型各具优劣，限制了预测系统的鲁棒性。CFA通过利用排名-评分特性（RSC）函数和模型间的认知多样性，在适度数量的多样且表现良好的模型中实现增强。本方法结合评分与排序双重信息，以及多种加权组合策略。采用均方根误差（RMSE）和平均绝对百分比误差（MAPE）作为关键评估指标。实验表明，该方法在比特币价格预测上取得0.19%的显著MAPE表现，不仅超越单一模型性能，也优于现有比特币预测模型。
原文摘要 · Abstract (English)
In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant profit. Every machine learning model has its own strength and weakness, which hinders progress toward robustness. CFA has been used to enhance models by leveraging rank-score characteristic (RSC) function and cognitive diversity in the combination of a moderate set of diverse and relatively well-performed models. Our method utilizes both score and rank combinations as well as other weighted combination techniques. Key metrics such as RMSE and MAPE are used to evaluate our methodology performance. Our proposal presents a notable MAPE performance of 0.19\%. The proposed method greatly improves upon individual model performance, as well as outperforms other Bitcoin price prediction models.
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