Paper: arXiv 2401.00534

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: 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.

Research Flowchart

  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