Financial Time-series Forecasting: Towards Synergizing Performance and Interpretability Within a Hybrid Machine Learning Approach

@article{1707159,
      author = {Kexin Wu, Chufeng Jiang},
      title = {Financial Time-series Forecasting: Towards Synergizing Performance and Interpretability Within a Hybrid Machine Learning Approach},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {8},
      pages = {296-303},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707159.pdf},
      abstract = {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.},
      month = {February},
  }