A data scientist must forecast three months of product sales. Analysis shows sales are seasonal, affected by holidays, and correlated with sales of other products in the category. Which approach requires the least development effort to incorporate seasonality, holidays, and related-item correlations into the forecast?

Amazon Forecast with DeepAR+ is the most efficient choice. DeepAR+ is a deep learning algorithm specifically designed for time series forecasting, excelling at capturing complex patterns like seasonality and trends across multiple related time series. Amazon Forecast's built-in Holidays featurization directly incorporates holiday effects without manual feature engineering. ARIMA, while capable of handling seasonality, is a statistical model less adept at learning from multiple related series and doesn't inherently handle holiday effects as seamlessly as DeepAR+ with the dedicated featurization. Using SageMaker Processing or GluonTS would require significant manual effort to engineer holiday features and manage the training pipeline, which contradicts the "least development effort" requirement.