Price-demand elasticity features in machine learning demand forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models for demand forecasting often fail to accurately detect and incorporate price-demand elasticity features due to confounding variables, leading to inaccurate cause-effect relationships and masking of causal effects.
Innovation Solution
A model training system that utilizes price-demand elasticity features to generate machine learning models, deconfounds input data through techniques like randomized controlled A/B group trials or independence weighting, to separate causal variables from confounding variables, enabling accurate demand forecasting and demand shaping by quantifying individual causal effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models use traditional demand forecasting methods without price-demand elasticity features, then the model structure remains simple, but the model fails to detect and incorporate significant outcome-predicting features leading to inaccurate predictions
Solution Approach 1:
The system performs preliminary calculations of price-demand elasticity features using historical data before training the machine learning model. This pre-computation of elasticity metrics (such as price elasticity coefficients and demand response indicators) allows the model to incorporate these significant predictive features without increasing structural complexity during the training phase, thereby improving prediction accuracy while maintaining manageable model complexity
Solution Approach 2:
The patent introduces price-demand elasticity features as intermediary variables that mediate between raw historical data (prices, demand, promotions) and the machine learning model predictions. These elasticity features serve as processed intermediaries that capture the causal relationship between price changes and demand responses, enabling the model to achieve higher accuracy without directly modeling complex causal structures
2Reliability
If machine learning models incorporate causal factors without deconfounding, then the model can identify potential causal relationships, but confounding variables mask or dilute the causal effects making it difficult to correctly identify cause-effect relationships
Solution Approach 1:
The system segments the analysis into two distinct phases: first, identifying potential causal relationships between variables (such as price and demand), and second, deconfounding the data to isolate true causal effects from confounding influences. This segmentation allows the model to systematically address confounding variables through techniques like difference-in-differences or instrumental variables, improving causal identification accuracy while managing processing complexity through structured methodology
Solution Approach 2:
The system performs preliminary deconfounding analysis on historical data before training the final prediction model. By pre-processing the data to remove confounding effects (such as seasonal patterns, promotional impacts, or external shocks) that could mask causal relationships, the model receives cleaned input data that enables more accurate identification of true cause-effect relationships without requiring complex deconfounding operations during each prediction cycle
Data Source
AI summary
A system and method are disclosed to identify one or more price-demand elasticity causal factors and to forecast demand using the one or more price-demand elasticity causal factors. Embodiments include a computer comprising a processor and memory. Embodiments train a machine learning model to identify one or more external causal factors that influence demand for one or more products. Embodiments train the machine learning model to generate one or more price-demand elasticity causal factors to predict a target outcome for a given product demand. Embodiments predict, with the machine learning model, a demand for the one or more products based, at least in part, on the identified one or more external causal factors and the generated one or more price-demand elasticity causal factors.