Metric Forecast Entity Relationship Machine Learning Model
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Solution Overview
Problem
Existing systems for generating metric forecasts for related entities in organizations suffer from inaccuracies, which impact business operations and economic aspects, as they fail to effectively account for the relationships between entities in their forecasting models.
Innovation Solution
A metric forecast entity relationship machine learning model is trained using historical data from primary and secondary entities to establish relationships, improving the accuracy of metric forecasts by employing advanced algorithms and optimizing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for metric forecasting, then processing speed is maintained, but forecast accuracy deteriorates due to failure to account for entity relationships
Solution Approach 1:
The system segments the forecasting problem by separating entity-specific forecasts into primary and secondary categories, then applies relationship modeling only where needed. This allows traditional simple models to handle independent entities while enhanced models handle related entities, improving accuracy without universally increasing complexity.
Solution Approach 2:
The patent introduces an intermediary relationship modeling layer that connects primary and secondary entity forecasts. This intermediary component captures inter-entity dependencies without requiring complete restructuring of the base forecasting system, thus improving accuracy while controlling complexity through modular addition.
2Measurement precision
If complex relationship modeling is applied to all entities, then forecast accuracy improves, but processing time increases
Solution Approach 1:
The system applies relationship modeling selectively only to entities that have identified relationships, rather than uniformly to all entities. Primary entities receive full relationship modeling treatment while secondary entities use simplified approaches, optimizing the balance between accuracy improvement and processing time consumption.
Solution Approach 2:
The patent implements partial relationship modeling by focusing computational resources on the most critical entity relationships (primary entities) while using simplified or omitted modeling for less critical relationships (secondary entities). This partial action approach achieves sufficient accuracy improvement without the full processing overhead of universal complex modeling.
3Device complexity
If entity relationships are not accounted for in forecasting models, then model simplicity is maintained, but forecast accuracy deteriorates impacting business operations
Solution Approach 1:
The forecasting system is segmented into independent and related entity components. Independent entities continue using simple models while related entities use enhanced relationship-aware models, thus maintaining overall system simplicity while improving accuracy where needed.
Solution Approach 2:
The system creates a universal forecasting framework that can handle both simple independent entity forecasting and complex related entity forecasting through a unified architecture. This multi-functional approach maintains model simplicity for straightforward cases while providing accuracy improvement capabilities when entity relationships are present.
Data Source
AI summary
A method for estimating metric forecasts associated with a plurality of related entities with more accuracy by training and applying a metric forecast entity relationship machine learning (ML) model is provided. The method includes obtaining a first primary and a first secondary entity metric forecast based on historical data of a primary entity metric obtained from primary entity metric device and historical data of secondary entity metric obtained from secondary entity metric device at different instances of time, training metric forecast entity relationship ML model based on relationship between first primary and first secondary entity metric forecast to obtain a trained metric entity relationship ML model that accounts for the relationship between the first primary entity metric forecast and the first secondary entity metric forecast, and estimating a second primary entity metric forecast and a second secondary entity metric forecast based on the trained metric entity relationship ML model.