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This simulation study quantifies selection bias in retail inflation estimation and compares correction methods, finding that stratification generally outperforms inverse probability weighting in long-tail contexts with severe positivity violations.
GNBAN is a new graph-based neural architecture for long-horizon retail demand forecasting that combines heterogeneous graph learning with an interpretable basis-decomposition forecasting head, achieving 4-5% improvement on Walmart and Favorita benchmarks.