A double DRL architecture selects forecasting models dynamically from a committee and introduces average-reward-convergence early stopping, demonstrating robustness on grocery sales and snack demand datasets.
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach.Expert Systems with Applications, 140:112896
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Designing a double deep reinforcement learning selection tool for resilient demand prediction
A double DRL architecture selects forecasting models dynamically from a committee and introduces average-reward-convergence early stopping, demonstrating robustness on grocery sales and snack demand datasets.