You are a machine learning specialist working for a retail clothing conglomerate. Your company sells many lines of clothing, such as budget casual wear, office casual wear, office formal wear, etc. For each of these existing products in these categories, you have been using autoregressive integrated moving average (ARIMA) models to forecast demand. You now wish to forecast demand for a new product based on the collective historical time series data from your existing products. Which approach should you take to forecast demand for your new product?

Answer options:

A.Forecast demand for your new product using the XGBoost algorithm.
B.Forecast demand for your new product using the DeepAR algorithm.
C.Forecast demand for your new product using an ARIMA model.
D.Forecast demand for your new product using k-means clustering.

Answer: B
Option A is incorrect. The XGBoost algorithm is used to solve regression, classification, and ranking problems. It would not be a good choice for a forecasting problem.
Option B is correct. The DeepAR Forecasting algorithm works great when you are trying to forecast using much similar time series across a set of cross-sectional units. That is exactly what you are trying to do: you have several similar time series from across your other product lines that you would now like to use to train your single model using all of these time series. The collective time series will help you predict sales for your new product.
Option C is incorrect. The ARIMA forecasting method uses one time series to train the model. You need to train using several time series. That’s where the DeepAR algorithm capabilities differentiate; DeepAR forecasts using many similar time series across a set of cross-sectional units.
Option D is incorrect. K-means clustering is used to find discrete groupings in your data. K-means does not fit your problem definition as well as the DeepAR algorithm.
Reference:
Please see the Amazon SageMaker developer guide titled K-Means Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/k-means.html), the Amazon SageMaker developer guide titled XGBoost Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html), the Amazon SageMaker developer guide titled DeepAR Forecasting Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html), and the Medium article titled Understanding Auto Regressive Moving Average Model — ARIMA (https://medium.com/fintechexplained/understanding-auto-regressive-model-arima-4bd463b7a1bb)