Which solution should a Machine Learning Specialist apply?

Explanations:

Training a custom ARIMA model for the new product is not ideal because ARIMA requires historical demand data for that specific product to make accurate forecasts. Since this is a new product with no prior sales data, ARIMA cannot be effectively utilized.

The Amazon SageMaker DeepAR algorithm is designed for time series forecasting and can handle situations where there is limited historical data, such as forecasting demand for a new product. It leverages recurrent neural networks and can learn from similar products’ sales history, making it suitable for this scenario.

The k-means clustering algorithm is not suitable for forecasting demand. It is primarily used for clustering and does not produce predictions for time series data. Therefore, it cannot forecast demand for a new product.

While XGBoost is a powerful regression algorithm that could potentially be used for demand forecasting with sufficient features, it still requires historical data for the specific product to make accurate predictions. As there is no historical data for the new product, this option is not appropriate.