Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach

ArXiv ID: 2401.00534 “View on arXiv”

Authors: Unknown

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.

Keywords: Time-series forecasting, Long-short term memory (LSTM), Linear regression (OLS/LASSO), Auto-correlational function, Model interpretability, Cryptocurrencies (Bitcoin)

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers
• Why: The paper introduces standard time-series techniques and basic machine learning models with minimal advanced mathematics, and it lacks concrete backtesting, statistical metrics, or code/data availability, placing it in the Philosophers quadrant.

  flowchart TD
    A["Research Goal<br>Predict Bitcoin Prices & Enhance Interpretability"] --> B["Data Preparation<br>Historical Bitcoin Price Data"]
    B --> C{"Hybrid ML Models Comparison"}
    C --> D["Linear Regression<br>OLS/LASSO"]
    C --> E["Long-Short Term Memory<br>LSTM"]
    C --> F["Decision Tree Regressor"]
    
    D --> G["Best Performance<br>Linear Regressor"]
    E --> G
    F --> G
    
    B --> H["Interpretability Analysis<br>Time-series Statistics"]
    H --> I["Decomposition<br>ACF<br>Exponential Triple Forecasting"]
    
    G --> J["Key Outcome<br>Synergizing Performance & Interpretability"]
    I --> J