Financial Time-Series Forecasting: Towards Synergizing Performance And
  Interpretability Within a Hybrid Machine Learning Approach
Shun Liu Kexin Wu Chufeng Jiang Bin Huang Danqing Ma
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.