Scenario: A manufacturer needs to forecast weekly sales for a brand-new product variant that has no sales history (cold-start problem). The model…

The scenario describes a classic cold-start forecasting problem where a model must be trained on existing product data to predict sales for a new product with no history. The Amazon SageMaker DeepAR algorithm is specifically designed for this use case. DeepAR trains a single model on multiple related time series (all SKUs), learning global patterns such as seasonality and trends. It can incorporate item metadata (e.g., brand, category) as features, allowing it to generate accurate forecasts for new items by associating them with patterns learned from existing, similar SKUs.

A. Random Cut Forest (RCF) is an unsupervised algorithm designed for anomaly detection, not for forecasting future values. It identifies unusual data points, which is irrelevant to this requirement.

B. While Linear Learner can perform regression, it is not inherently designed to capture complex time-series dynamics and dependencies across multiple series without significant, complex feature engineering.

D. K-means is an unsupervised clustering algorithm. While it can group similar SKUs, it does not perform forecasting itself. This would be an indirect, multi-step process requiring a separate forecasting model.

1. Amazon SageMaker Developer Guide, "DeepAR Forecasting Algorithm": "When your dataset contains hundreds of related time series, you can benefit from training a single DeepAR model on all of the time series together. DeepAR is especially useful when the time series have similar patterns... After the model is trained... you can also use it to predict the future for new time series that are similar to the ones in the training dataset." This directly addresses the cold-start problem and the need to learn shared patterns.

2. Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181-1191. In Section 3.1, the paper details how the model uses covariates, including item-specific features, which allows it to generalize to items not seen during training. This is the core mechanism for solving the cold-start problem. (DOI: https://doi.org/10.1016/j.ijforecast.2019.07.001)

3. Amazon SageMaker Developer Guide, "How the K-Means Algorithm Works": The documentation describes K-Means as an algorithm that "attempts to find discrete groupings within data." It makes no mention of forecasting capabilities, confirming it is not a direct solution for this problem.

4. Amazon SageMaker Developer Guide, "Random Cut Forest (RCF) Algorithm": The documentation states, "The Amazon SageMaker Random Cut Forest (RCF) algorithm is an unsupervised algorithm for detecting anomalous data points within a data set." This confirms its purpose is anomaly detection, not forecasting.