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This paper introduces a unified decision-theoretic pretraining framework for neural network-based time series estimators, trained on stratified simulations to approximate near-optimal decision rules. Experiments show that the resulting estimators outperform traditional methods like maximum likelihood estimation on both synthetic and real-world benchmarks.