Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations

TL;DR

This work introduces a novel interpretability method for large hierarchical probabilistic time-series forecasting, adapting generic interpretability techniques while addressing challenges associated with hierarchical structures and uncertainty.

Abstract

Hierarchical time-series forecasting is essential for demand prediction across various industries. While machine learning models have obtained significant accuracy and scalability on such forecasting tasks, the interpretability of their predictions, informed by application, is still largely unexplored. To bridge this gap, we introduce a novel interpretability method for large hierarchical probabilistic time-series forecasting, adapting generic interpretability techniques while addressing challenges associated with hierarchical structures and uncertainty. Our approach offers valuable interpretative insights in response to real-world industrial supply chain scenarios, including 1) the significance of various time-series within the hierarchy and external variables at specific time points, 2) the impact of different variables on forecast uncertainty, and 3) explanations for forecast changes in response to modifications in the training dataset. To evaluate the explainability method, we generate semi-synthetic datasets based on real-world scenarios of explaining hierarchical demands for over ten thousand products at a large chemical company. The experiments showed that our explainability method successfully explained state-of-the-art industrial forecasting methods with significantly higher explainability accuracy. Furthermore, we provide multiple real-world case studies that show the efficacy of our approach in identifying important patterns and explanations that help stakeholders better understand the forecasts. Additionally, our method facilitates the identification of key drivers behind forecasted demand, enabling more informed decision-making and strategic planning. Our approach helps build trust and confidence among users, ultimately leading to better adoption and utilization of hierarchical forecasting models in practice.

Figures (11)

• Figure 1: In real-world HTSF tasks, stakeholders and planners want to know: RQ1: which input variable(s) contribute most to the prediction; RQ2: which time steps of each variable contribute most to the prediction; RQ3: why prediction results change when input data changes.
• Figure 2: Subtree approximation allows HiereInterpret to accurately capture importance scores of any time-series across the hierarchy
• Figure 3: Calculate importance scores for forecasting node $x_i$ via subtree approximation
• Figure 4: Adapting to any probabilistic forecast by deriving importance scores from quantiles of the forecasts
• Figure 5: Probabilistic evaluation results of each quantile. While the 70th quantile with subtree has the best absolute performance, the 90th quantile receives the most improvement by introducing subtree approximation.