Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling

Key points

• We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own history.1
• FEDE quantifies how the full signed forecast-error distribution changes across prediction horizons.1
• Controlled nonlinear benchmarks hide source-target dependence behind nonlinear transformations not supplied to the predictors.1
• Together, these results establish FEDE as a sensitive and robust horizon-resolved tool that extracts predictive information beyond conventional error-magnitude measures when the nonlinear interaction form is unknown.1

Sources (1)

• [1]Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 01:40 PM

  We compare matched out-of-sample target-only and target-plus-source forecasts and introduce the forecast-error distribution entropy (FEDE) predictive-causality framework, where predictive causality is understood in the Granger sense: auxiliary history is informative when it improves prediction beyond the target's own past.

  We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own history.

Extractive summary: sentences quoted from the sources.