Hierarchical Ensemble Forecasting for Demand Drift
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Solution Overview
Problem
Traditional machine learning solutions for demand forecasting in complex supply chains with fluctuating demand are ineffective due to data drift and covariance shift, leading to inaccurate and unreliable predictions, especially in hierarchical systems with multiple processes and machines.
Innovation Solution
A Hierarchical Ensembling and Customized Optimization (HECO) model architecture that combines forecasts from multiple machine learning models with different hyperparameters and features, optimizing results using a probabilistic approach to minimize validation errors under physical and operational constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional machine learning models are used for demand forecasting, then the forecasting process can be automated, but the prediction accuracy deteriorates due to data drift and covariance shift in fluctuating demand scenarios
Solution Approach 1:
The patent combines multiple machine learning models from different families (e.g., ARIMA, exponential smoothing, neural networks, tree-based models) into an ensemble forecasting system. Each model captures different patterns in the data, and their predictions are aggregated to produce a final forecast that is more robust to data drift and covariance shift than any individual model, thereby maintaining automation while improving prediction accuracy
Solution Approach 2:
The system dynamically adjusts model parameters and hyperparameters based on the characteristics of the input data and performance metrics. This includes adapting smoothing parameters, retraining intervals, and model weights in response to changing demand patterns, allowing the automated system to maintain accuracy despite data drift and covariance shift over time
2Device complexity
If simple individual prediction models are used for each process or machine, then the model complexity is reduced, but the overall system performance deteriorates due to lack of collaborative analysis in hierarchical systems
Solution Approach 1:
The forecasting system is segmented into multiple hierarchical levels, with individual models assigned to specific processes or machines, and higher-level models that aggregate and coordinate these individual predictions. This segmentation allows each component to remain relatively simple while the hierarchical structure enables collaborative analysis that improves overall system performance
Solution Approach 2:
The patent implements a nested model architecture where individual process or machine-level prediction models are embedded within higher-level aggregate models. The individual models capture local patterns, while the higher-level models capture system-wide patterns and coordinate the individual predictions, creating a nested structure that improves productivity without requiring each individual model to be overly complex
Data Source
AI summary
According to an embodiment, a method for generating a forecast of a timeseries is disclosed. The method comprises receiving a set of features comprising data and timeseries to be used by each of a plurality of prediction models for generating the forecast. Further, the method comprises generating using the set of features, a plurality of forecast results based on an ensemble of the plurality of prediction models. Furthermore, the method comprises optimizing the plurality of forecast results associated with a respective forecast module. Additionally, the method comprises probabilistically combining the outputs of the plurality of optimization modules. Moreover, the method comprises outputting a final forecast based on the combination of the at least two forecast results.