Machine Learning Opportunity Cost Prediction Without Demand Forecasting
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Solution Overview
Problem
Conventional computing systems for revenue management face challenges in accurately predicting opportunity costs due to insufficient data and demand forecasting inaccuracies, especially in industries with high demand volatility and perishable resources, leading to suboptimal pricing decisions and revenue loss.
Innovation Solution
A machine learning model is trained to estimate opportunity costs directly from historical transaction data, using a process that generates an opportunity cost proxy as a function of remaining capacity and time to expiration, eliminating the need for demand forecasting and reducing data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If demand forecasting is used to predict opportunity costs, then pricing decisions can be made, but the predictions are inaccurate due to data insufficiency and demand volatility
Solution Approach 1:
The patent creates a proxy for opportunity cost by copying and analyzing historical transaction patterns. Instead of directly forecasting future opportunity costs, the system learns from historical data how prices and demand correlate, then applies these learned patterns to predict opportunity costs in new situations where direct forecasting would fail due to data insufficiency
Solution Approach 2:
The patent replaces the traditional mechanical demand forecasting system with a machine learning-based opportunity cost prediction system. Rather than attempting to model complex demand dynamics directly, the system substitutes this with learning from historical transaction data, allowing it to capture nonlinear relationships and patterns that traditional forecasting methods miss
2Productivity
If traditional demand forecasting methods are used, then pricing decisions can be made, but computational resources are excessive and biases propagate
Solution Approach 1:
The patent extracts only the essential patterns needed for opportunity cost prediction from historical data, rather than processing complete demand forecasts. By taking out only the relevant signal (the relationship between prices, demand, and opportunity costs) and discarding unnecessary computational steps, the system achieves both efficiency and bias reduction
Solution Approach 2:
Instead of forecasting demand and then deriving opportunity costs, the patent inverts the approach by directly predicting opportunity costs from historical transaction data. This inversion eliminates the intermediate demand forecasting step that introduces computational overhead and potential biases, while still providing the necessary pricing information
Data Source
AI summary
Techniques for training a prediction model are disclosed. An example method includes processing historical event data comprising a plurality of computer-readable events for a resource to determine previous parameters for the resource. The method also includes generating training data for training the prediction model without using a forecast for future utilization of the resource. The training data comprises a set of proxies generated from previous parameters for the resource. Each proxy is associated with a remaining capacity of the resource and a remaining time to expiration of the resource. The method also includes training the prediction model to generate a mapping from the remaining capacity of the resource and the remaining time to expiration of the resource to the proxy. The method also includes receiving a request that describes a potential future event pertaining to the resource and generating a prediction for the potential future event using the prediction model.