In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered
substantial attention due to its potential impact on financial markets and
investment strategies. This paper propose a comparative study on hybrid machine
learning algorithms and leverage on enhancing model interpretability.
Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM),
decision tree regressors are introduced. Through the grounded experiments, we
observe linear regressor achieves the best performance among candidate models.
For the interpretability, we carry out a systematic overview on the
preprocessing techniques of time-series statistics, including decomposition,
auto-correlational function, exponential triple forecasting, which aim to
excavate latent relations and complex patterns appeared in the financial
time-series forecasting. We believe this work may derive more attention and
inspire more researches in the realm of time-series analysis and its realistic
applications.

比特币价格的预测引起了在金融市场和投资策略上的关注，本研究提出了一个关于混合机器学习算法在提高模型可解释性方面的比较研究。通过实证实验，我们发现线性回归模型在候选模型中表现最好。对于模型的解释性，我们对时间序列统计的预处理技术进行系统概述，包括分解、自相关函数、指数三重预测等，旨在挖掘金融时间序列中的潜在关系和复杂模式。我们相信这项工作将在时间序列分析及其实际应用领域引起更多关注，激发更多的研究。