Which solution will meet these requirements?

Explanations:

The ARIMA algorithm is not suitable for incorporating item-related features and time series binary features like “promotion applied?” which are necessary for this problem. Additionally, ARIMA does not handle the 30-day prediction cycle or the complex dependencies between features well.

Similar to Option A, ARIMA is not appropriate for this use case due to its limitations with complex features like binary promotion data and item-related features. WAPE is also not the best metric for this scenario, as it doesn’t consider quantile losses, which are important in predicting stock levels.

The CNN-QR algorithm is a good choice because it is capable of handling multiple input features, including item-related and binary time series features. The Weighted Quantile Loss (wQL) metric at 0.75 is appropriate for this problem as it focuses on minimizing the error for higher demand forecasts, which is crucial when managing stock levels to avoid running out of inventory.

While the CNN-QR algorithm is a good choice, WAPE is not the ideal metric for this situation. The problem’s focus on minimizing stockouts and maximizing profit requires a more nuanced metric like wQL, which is better suited for forecasting demand at specific quantiles. WAPE does not provide the necessary focus on higher-demand scenarios.