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

• Author(s): Kexin Wu ; Chufeng Jiang
• Paper ID: 1707159
• Page: 296-303
• Published Date: 14-02-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 8 February-2025

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.

Citations

IRE Journals:
Kexin Wu , Chufeng Jiang "Financial Time-series Forecasting: Towards Synergizing Performance and Interpretability Within a Hybrid Machine Learning Approach" Iconic Research And Engineering Journals Volume 8 Issue 8 2025 Page 296-303

IEEE:
Kexin Wu , Chufeng Jiang "Financial Time-series Forecasting: Towards Synergizing Performance and Interpretability Within a Hybrid Machine Learning Approach" Iconic Research And Engineering Journals, 8(8)