arXiv:2606.08896v1 Announce Type: new Abstract: Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability. This paper studies forecastability-aware expert routing: learning how data characteristics determine the suitability of forecasting experts. We propose \method{}, a sparse
The increasing complexity and scale of real-world AI applications, especially in areas like retail and industry, demand more sophisticated and efficient forecasting models capable of handling heterogeneous data.
This research addresses a critical challenge in AI: improving the accuracy and efficiency of forecasting systems across diverse and complex datasets, which is fundamental for optimizing operations in various sectors.
Traditional monolithic forecasting models or dense ensembles will be increasingly replaced by more intelligent, 'forecastability-aware' systems that dynamically select or combine expert models, leading to better decision-making and resource allocation.
- · Retail and e-commerce companies
- · Industrial manufacturers
- · Logistics and supply chain management
- · Machine learning researchers and developers
- · Companies relying on single, static forecasting models
- · Inefficient AI systems with high inference costs
Forecasting systems become more accurate and computationally efficient in heterogeneous environments.
Improved forecasting leads to optimized inventory management, reduced waste, and more agile operational planning for large enterprises.
The enhanced capability for AI to handle diverse real-world data patterns could accelerate automation in new sectors, impacting labor markets and operational structures.
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Read at arXiv cs.AI