Dynamic Feature Selection for Demand Forecasting Models
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Solution Overview
Problem
Traditional demand forecasting methods struggle to accurately predict product demand due to their inability to adapt to changing market conditions and handle a large number of relevant features, leading to inaccurate forecasts and increased costs or lost revenues.
Innovation Solution
A computer system dynamically selects an optimized feature set for demand forecasting by designating mandatory and optional features, forming training and validation datasets, and using machine learning algorithms to generate trained models, which are then combined for a final demand forecast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time series analysis or regression methods are used for demand forecasting, then the forecasting process is simple, but the forecast accuracy deteriorates when market conditions change or when many factors are considered
Solution Approach 1:
The patent implements dynamic feature selection that adapts to changing market conditions. The system automatically selects relevant features based on current data patterns rather than using a fixed set of features, allowing the forecasting model to dynamically adjust to market changes while maintaining manageable complexity through automated feature selection.
Solution Approach 2:
The patent changes the parameters of the forecasting approach by transitioning from traditional fixed feature sets to dynamic, data-driven feature selection. The system evaluates and selects features based on their relevance to current demand patterns, changing the fundamental parameters of how features are chosen and weighted in the forecasting model.
2Measurement precision
If all relevant features are included in the forecasting model, then the forecast accuracy may improve, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant features from the complete set of available features for each forecasting iteration. By selecting a subset of features that are most predictive of demand at any given time, the system achieves accurate forecasts while reducing processing time and computational resources compared to using all available features.
Solution Approach 2:
The patent applies partial action by selecting only the necessary subset of features rather than processing all available features. The dynamic feature selection identifies and uses only the features that contribute most to forecast accuracy at any given time, avoiding the computational overhead of processing excessive or irrelevant features.
3Adaptability or versatility
If historical demand information alone is used for forecasting, then the forecasting method is simple, but the adaptability to changing market conditions deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-identifying and selecting relevant features before the actual forecasting process. The system prepares a dynamic feature set based on historical patterns and current conditions, so that when forecasting is needed, the relevant features are already selected and ready, enabling rapid adaptation to market changes without complex real-time processing.
Solution Approach 2:
The patent implements feedback mechanisms where the forecasting system continuously evaluates the performance of selected features and adjusts the feature set based on actual market outcomes. This feedback loop allows the system to adapt to changing market conditions by learning from past performance and refining feature selection, maintaining accuracy while managing complexity through iterative improvement.
Data Source
AI summary
Embodiments generate a model of demand of a product that includes an optimized feature set. Embodiments receive sales history for the product and receive a set of relevant features for the product and designate a subset of the relevant features as mandatory features. From the sales history, embodiments form a training dataset and a validation dataset and randomly select from the set of relevant features one or more optional features. Embodiments include the selected optional features with the mandatory features to create a feature test set. Embodiments train an algorithm using the training dataset and the feature test set to generate a trained algorithm and calculate an early stopping metric using the trained algorithm and the validation dataset. When the early stopping metric is below a predefined threshold, the feature test set is the optimized feature set.