We’re excited to launch explainability impact scores in Amazon Forecast, which help you understand the factors that impact your forecasts for specific items and time durations of interest. Forecast is a managed service for developers that uses machine learning (ML) to generate more accurate demand forecasts, without requiring any ML experience. To increase forecast model accuracy, you can add additional information or attributes such as price, promotion, category details, holidays, or weather information to your forecasting model, but you may not know how each attribute influences your forecast. With today’s launch, you can now understand how each attribute impacts your forecasted values using the explainability feature, which we discuss in this post.
ML-based forecasting models, which are more accurate than heuristic rules or human judgment, can drive significant improvement in revenue and customer experience. However, business leaders often lose trust in technology when they see forecasted numbers drastically differing from

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