A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company. Most of the data in the training…

Imputing missing values using linear regression is a more complex method that requires additional development and computational effort compared to simply using the mean value.

While Amazon SageMaker DeepAR can handle NaN values by ignoring them in the loss function during training, if "most" of the data is missing, this approach might lead to insufficient learning and doesn't actively "account for" the missing values through imputation.

Imputing missing values using a forward fill method requires specific handling of time series data and can introduce bias if not applied carefully, demanding more development effort than a simple mean imputation.

Imputing missing values with the mean is a simple and common preprocessing technique that requires minimal development effort, allowing the Amazon SageMaker DeepAR model to have a continuous target variable for training when most of the original data is missing.