Measure of Dependence for Financial Time-Series

ArXiv ID: 2311.12129 “View on arXiv”

Authors: Unknown

Abstract

Assessing the predictive power of both data and models holds paramount significance in time-series machine learning applications. Yet, preparing time series data accurately and employing an appropriate measure for predictive power seems to be a non-trivial task. This work involves reviewing and establishing the groundwork for a comprehensive analysis of shaping time-series data and evaluating various measures of dependence. Lastly, we present a method, framework, and a concrete example for selecting and evaluating a suitable measure of dependence.

Keywords: Time Series Data, Dependence Measures, Predictive Power, Data Preparation, Machine Learning Frameworks, General (Cross-Asset)

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper focuses on theoretical frameworks and mathematical definitions for measuring dependence in time series, with advanced discussions on tensor operations and statistical concepts, but lacks code, specific backtests, or datasets in the provided excerpt, placing it in the theoretical Lab Rats quadrant.

  flowchart TD
    A["Research Goal: <br>Find Optimal Dependence Measure for Financial Time-Series"] --> B["Data Input: <br>Cross-Asset Financial Data"]
    B --> C["Methodology: <br>Review & Establish Data Shaping Framework"]
    C --> D["Computational Process: <br>Evaluate Measures of Dependence"]
    D --> E{"Measure Selection & <br>Evaluation Loop"}
    E --> D
    E --> F["Outcomes: <br>1. Concrete Framework for Selection<br>2. Comparative Analysis of Measures<br>3. Final Recommendation"]