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Context Based Predictive Information.

Yuval Shalev1, Irad Ben-Gal1

  • 1Laboratory for AI, Machine Learning, Business & Data Analytics, Department of Industrial Engineering, The Tel-Aviv University, Ramat-Aviv 6997801, Israel.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

We introduce a new algorithm, context-based predictive information (CBPI), for improved time series analysis. This method enhances predictive information estimation, especially in sparse conditions, by focusing on informative sequences.

Keywords:
context treeinformation bottleneckpredictive informationtime series analysis

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Area of Science:

  • Time Series Analysis
  • Information Theory
  • Machine Learning

Background:

  • Estimating predictive information (PI) is crucial for understanding time series dynamics.
  • Existing methods struggle with sparse predictive information (SPI) conditions where informative sequences are rare.

Purpose of the Study:

  • To propose a novel algorithm, context-based predictive information (CBPI), for accurate PI estimation.
  • To address the limitations of current methods in handling SPI conditions.
  • To explore the relationship between CBPI and the information bottleneck theory.

Main Methods:

  • Developed a lossy compression-based algorithm (CBPI) for PI estimation.
  • Implemented CBPI on a real-world dataset of U.S. large bank stock prices.
  • Analyzed the theoretical connection between CBPI and the deterministic information bottleneck.

Main Results:

  • CBPI demonstrates superior PI estimation compared to benchmark methods, particularly under SPI conditions.
  • The algorithm effectively ignores uninformative sequences, thereby improving estimation accuracy.
  • CBPI enhances explainability by identifying informative sequences within time series data.

Conclusions:

  • CBPI offers a more effective approach to estimating predictive information in time series.
  • The method shows practical utility in financial market analysis.
  • CBPI provides a new perspective on the information bottleneck framework.