Ensemble Data Prediction for Seasonality and Uncertainty Control

The patent combines multiple machine learning models (XGBoost, Bayesian models, linear regression) into an ensemble prediction system. This merging of multiple models addresses the contradiction by maintaining relatively simple individual models while achieving high stability and reliability through their combined predictive power, directly resolving the issue of heuristic models being simple but unstable.

The prediction system uses a composite approach by integrating different types of machine learning models (tree-based XGBoost, probabilistic Bayesian models, and linear models) into a unified prediction framework. This composite structure allows the system to leverage the strengths of each model type, achieving both simplicity and reliability simultaneously.

The patent segments the prediction task into distinct components: an XGBoost model for capturing non-linear patterns, Bayesian models for uncertainty quantification, and linear regression for baseline trends. This segmentation allows each model to specialize in specific aspects of data pattern recognition, improving overall measurement precision while maintaining ease of operation through modular implementation.

The system dynamically selects and weights different model predictions based on the characteristics of the input data and their respective uncertainties. This dynamic approach enables the system to adapt to different data patterns and seasonality types, significantly improving pattern identification accuracy while keeping the implementation straightforward through automated model selection.

The patent implements a tiered prediction approach where simpler models (linear regression) provide baseline predictions with lower computational cost, while more complex models (XGBoost, Bayesian) are applied selectively to capture additional patterns and reduce errors. This partial application of complex models maintains reasonable computational resource consumption while significantly improving prediction accuracy.

The system replaces traditional single-model mechanical prediction approaches with a probabilistic ensemble system that substitutes deterministic predictions with uncertainty-aware predictions. This substitution reduces errors by quantifying and managing prediction uncertainty, achieving higher accuracy without proportionally increasing computational resource consumption.