You need to use Amazon SageMaker DeepAR to forecast required input materials and energy, but most training records have missing target values and the data is stored as JSON. Which approach requires the least development effort to handle the missing target values for training DeepAR?

The correct approach is to encode missing target values as NaN because Amazon SageMaker DeepAR is designed to handle missing values in the target time series directly during training. This built-in capability means you don't need to perform manual imputation, which significantly reduces development effort and avoids introducing potential biases or inaccuracies from imputation methods. Linear regression imputation, forward-fill, and replacing with the mean are all valid imputation techniques, but they require additional development steps to implement and might not be as effective as DeepAR's native handling of missing data, especially for complex time series patterns. Since DeepAR can process NaN values, these imputation steps are unnecessary and add complexity.