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Configures an MLForecast pipeline using LightGBM on Polars DataFrames for weekly time series forecasting. Includes specific lag features (1,2,3,6,12), rolling window statistics (mean/std), and date features, while avoiding expanding means and handling Polars-specific date attribute errors.
Configure and execute a time series forecasting pipeline using Polars for data manipulation and MLForecast with LightGBM for modeling, applying specific lag features, rolling statistics, and evaluation metrics.
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