Rule-based model selection in time series forecasting achieves low accuracy and exhibits high ranking instability across data regimes and forecasting horizons.
Explaining nonlinear classification decisions with deep taylor decomposition
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Why Model Selection Fails in Time Series Forecasting: An Empirical Study of Instability Across Data Regimes
Rule-based model selection in time series forecasting achieves low accuracy and exhibits high ranking instability across data regimes and forecasting horizons.