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Model-free quantification of time-series predictability.

Joshua Garland1, Ryan James1, Elizabeth Bradley2

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Summary
This summary is machine-generated.

Forecast model performance depends on time series complexity. Weighted permutation entropy quantifies this complexity, helping practitioners choose appropriate forecasting strategies for improved prediction accuracy.

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Area of Science:

  • Time Series Analysis
  • Complexity Science
  • Predictive Modeling

Background:

  • Real-world time series exhibit complexity due to high dimensionality, nonlinearity, nonstationarity, noise, and data limitations.
  • This complexity often challenges the effectiveness of standard forecasting strategies.
  • Quantifying time series complexity is crucial for understanding prediction limitations.

Purpose of the Study:

  • To investigate the relationship between time series complexity and forecast strategy failure.
  • To identify quantifiable measures of complexity that correlate with predictability.
  • To develop a practical heuristic for selecting appropriate forecasting methods.

Main Methods:

  • Studied 120 diverse time-series datasets.
  • Employed various forecasting models to generate predictions.
  • Utilized weighted permutation entropy to estimate time series redundancy and complexity.
  • Compared prediction accuracy with estimated complexity measures.

Main Results:

  • Empirically demonstrated a correlation between time series complexity and forecast predictability.
  • Showed that weighted permutation entropy effectively measures complexity and predictive structure.
  • Identified cases where forecast methods are mismatched to the time series' inherent structure.

Conclusions:

  • Time series complexity is a key factor determining forecast success.
  • Weighted permutation entropy serves as a reliable complexity metric.
  • A model-free heuristic can guide practitioners in selecting suitable forecasting approaches based on complexity.