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Setting Limits on Supersymmetry Using Simplified Models
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Predictability limit of partially observed systems.

Andrés Abeliuk1,2, Zhishen Huang3, Emilio Ferrara4

  • 1Information Sciences Institute, University of Southern California, Marina del Rey, CA, 90292, USA.

Scientific Reports
|November 25, 2020
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Summary
This summary is machine-generated.

Predicting dynamic systems is harder with less data. Sampling frequency significantly degrades forecast accuracy, even with external data, revealing inherent predictability limits in partially observed systems.

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Area of Science:

  • Complex Systems Science
  • Data Science
  • Predictive Analytics

Background:

  • Accurate forecasting of dynamic systems is crucial across diverse fields like finance, epidemiology, and cybersecurity.
  • Many real-world systems are only partially observed, posing significant challenges for predictive modeling.

Purpose of the Study:

  • To demonstrate and quantify the impact of temporal sampling on system predictability.
  • To investigate whether external signals can recover lost predictability.
  • To establish fundamental limits on predictability in partially observed dynamic systems.

Main Methods:

  • Developed a theoretical framework to analyze the relationship between sampling frequency and predictability.
  • Quantified the loss of predictability as a function of sampling.
  • Validated findings using real-world datasets from infectious disease outbreaks, online discussions, and software development projects.

Main Results:

  • System predictability demonstrably degrades with decreased temporal sampling, irrespective of the forecasting model used.
  • The loss of predictability due to sampling cannot be compensated for by incorporating external signals.
  • Empirical validation across diverse real-world systems confirmed the theoretical predictions.

Conclusions:

  • Temporal sampling is a fundamental constraint on the predictability of dynamic systems.
  • Partial observability inherently limits forecasting accuracy, a decay that is irreversible.
  • Understanding these predictability limits is essential for realistic forecasting in applied domains.