FINANCIAL TIME-SERIES FORECASTING - TOWARDS SYNERGIZING PERFORMANCE AND INTERPRETABILITY WITHIN A HYBRID MACHINE LEARNING APPROACH

Aug 5, 2026

Upload by Ivy

Cryptocurrency research highlights the importance of predicting Bitcoin prices for investment decisions and potential market effects. This paper presents a comparative study of hybrid machine learning approaches, focusing on both predictive performance and interpretability. Candidate models include ordinary least squares and LASSO linear regression, LSTM, and decision-tree regressors. Experiments indicate linear regression achieves the strongest performance. For interpretability, the work reviews time-series preprocessing and statistics, including decomposition, autocorrelation, and exponential triple forecasting, to uncover latent relationships and complex patterns.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Financial-market motivation and data challenges

Time-series preprocessing for forecasting

Model comparison and hybrid learning perspective

arXiv :2401 .00534v1 [ cs .LG] 31 Dec 2023 FINANCIAL TIME-SERIES FORECASTING: TOWARDS SYNERGIZING PERFORMANCE AND INTERPRETABILITY WITHIN A HYBRID MACHINE LEARNING APPROACH Shun Liu 1†∗ [kevinliuleo@gmail.com](kevinliuleo@gmail.com) Kexin Wu5 [kw634@cornell.edu](kw634@cornell.edu) Chufeng Jiang4 [chufeng.jiang@utexas.edu](chufeng.jiang@utexas.edu) Bin Huang3 [bin@smu.edu](bin@smu.edu) Danqing Ma2 [3530761316qq@gmail.com](3530761316qq@gmail.com) 1Department of Computer Science, Shanghai University of Finance and Economics, Shanghai, China 2Department of Computer Science, Stevens of Institute of technology, Hoboken, USA 3Electrical and computer engineering, Southern Methodist University, Dallas, USA 4Department of Computer Science, The University of Texas at Austin, Texas, USA 5Independent Researcher, New York, USA 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. 1 Introduction The evolution of machine learning has fundamentally transformed a myriad of fields, showcasing its remarkable versatility and power in tackling complex problems. From traffic sign recognition [1] and cancer gene data classification [2], to the challenges of autonomous navigation at unsignalized intersections [3] and real-world storm prediction [4], the impact of machine learning is profound and far-reaching. These diverse applications demonstrate the capability of machine learning to not only analyze but also predict and interpret vast and complex datasets across various domains. In transportation, for instance, machine learning models have been instrumental in improving safety and efficiency, as seen in traffic sign recognition systems [1] . Similarly, in healthcare, deep learning techniques have enabled significant advances in understanding genetic data, thereby enhancing cancer diagnosis and treatment [2] . The field of autonomous navigation has also benefited greatly, with reinforcement learning and model predictive control approaches playing a crucial role in navigating complex environments [3] . Moreover, the predictive power of machine learning is exemplified in meteorology, where advanced models are used for accurate storm prediction, aiding in disaster preparedness and response [4] . The application of machine learning extends to the realm of urban planning and transportation, where deep sequential models have been used for utility-based route choice behavior modeling [5] . This illustrates how machine learning ∗First Author †Corresponding Author can assist in understanding and predicting human behavior in complex urban environments. Furthermore, the field of time-series analysis has seen significant advancements through machine learning, particularly in dealing with nonlinear and non-stationary data, as demonstrated by Huang et al. [6] . This is particularly relevant in financial markets, where time-series data is abundant and complex. In this report, we focus on the application of machine learning in the financial sector, particularly in the realm of cryptocurrencie

FAQs

How does the paper address interpretability?

It provides a systematic overview of time-series preprocessing and statistical techniques such as decomposition, autocorrelation, and exponential triple forecasting to reveal latent relations and complex patterns.

Aug 5, 2026

Upload by Ivy