Advancing Operational Intelligence Forecasting Through ML-Based Meta-Models and Foundation Models
Future-proof forecasts: How to use ML meta models and foundation models for operational intelligence
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ML meta models and foundation models
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Highest forecast accuracy: The superior performance of ML-based meta-models, in particular gradient boosting algorithms (XGBoost, LightGBM), for predicting hourly sales figures.
Robust scalability: The efficiency and stability of the hybrid PySpark Pandas approach for horizontal scaling and periodic retraining in large, distributed systems.
Minimal feature engineering: The emerging potential of foundation models (Chronos, TimesFM) to achieve competitive accuracy through zero-shot inference (time series as input only).
Real challenge: Benchmarking 12 models based on hourly sales data over a 14-day horizon of thousands of German restaurants.
Specific recommendations: Clear recommendations for action as to whether ML meta models (for robustness without GPU) or foundation models (for simplicity with GPU) are the optimal strategy for your company.
Feature engineering: (weather, calendar, time pattern) and the need for minimal preprocessing for foundation models.
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