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.

翻译：本研究提出将一种新的模型融合与学习范式——组合融合分析（CFA）应用于比特币价格预测领域。金融产品的价格预测一直是金融学的重要课题，成功的价格预测能带来显著收益。每种机器学习模型皆有其优势与局限，这制约了模型鲁棒性的提升。CFA通过利用排序-评分特征（RSC）函数及认知多样性，对一组具有差异性且表现相对良好的模型进行组合，从而增强模型性能。本方法综合运用评分组合、排序组合及其他加权组合技术，并采用RMSE与MAPE等关键指标评估方法性能。所提方案取得了0.19\%的显著MAPE表现，不仅较单一模型性能有大幅提升，也优于其他比特币价格预测模型。