Automated Forecasting With Preconfigured Scenario Assumptions
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Solution Overview
Problem
Existing forecasting systems are complex, leading to user frustration, errors, and high barriers of entry, with inefficient human-machine interaction and limited understanding of model interpretation, often resulting in incorrect or nonsensical results.
Innovation Solution
A forecasting tool that utilizes a plurality of data files with predefined assumption metrics, allowing for rapid and efficient generation of multiple scenario forecasts with minimal user input, including default data files for automation and compliance checks, and enabling comparisons between scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If highly configurable forecasting systems are used to provide detailed customization options, then adaptability to different forecasting scenarios is improved, but device complexity increases and user frustration increases
Solution Approach 1:
The system pre-configures forecasting models with default parameters and assumptions before users need to use them. This preliminary setup eliminates the need for users to configure complex model parameters from scratch, reducing both the perceived complexity and the actual configuration requirements while maintaining adaptability through pre-built scenario templates.
Solution Approach 2:
The system automatically generates forecasts by self-executing predefined models without requiring manual configuration input from users. The automated forecasting engine applies selected scenarios and assumptions automatically, transforming a previously manual configuration process into an autonomous operation that maintains versatility while eliminating user frustration with complex configuration interfaces.
2Adaptability or versatility
If highly configurable forecasting systems are used to provide detailed customization options, then adaptability to different forecasting scenarios is improved, but the likelihood of user errors increases
Solution Approach 1:
The system pre-validates and pre-configures all forecasting models with correct parameter relationships and assumptions before users access them. This preliminary preparation ensures that when users select pre-configured scenarios, the underlying calculations are already correct, eliminating user errors while maintaining scenario adaptability.
Solution Approach 2:
The automated forecasting system executes predefined, validated models without requiring user configuration input, thereby eliminating human errors in parameter entry and calculation. The system self-applies correct assumptions and parameters, ensuring reliable forecasting results while maintaining the ability to adapt to different scenarios through pre-built model templates.
3Measurement precision
If complex forecasting models are used to provide comprehensive analysis, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system pre-configures complex forecasting models with correct parameter settings and assumptions before users need them. This preliminary setup preserves the measurement precision of complex models while hiding their operational complexity from users, who simply need to select pre-configured scenarios rather than configure complex parameters themselves.
Solution Approach 2:
The automated forecasting system executes complex models autonomously without requiring user interaction for parameter configuration. The system self-applies complex calculations and assumptions, delivering precise forecasting results while maintaining ease of operation through automated execution rather than manual configuration.
4Ease of manufacture
If tightly scoped forecasting systems are used to simplify implementation, then ease of manufacture is improved, but loss of information occurs
Solution Approach 1:
The system implements a universal forecasting platform that can handle multiple scenario types and complexity levels through a single unified architecture. This multi-functional design maintains implementation simplicity through standardized core functionality while preventing information loss by accommodating diverse forecasting needs through configurable scenario templates that preserve all necessary data and assumptions.
Data Source
AI summary
A system, method, and computer readable medium for forecasting multiple scenarios are disclosed. Illustratively, the method includes providing data files each comprising a plurality of assumption metrics used for forecasting. Each of the data files can be associated with different scenarios or with the same scenario with different assumption metrics. Data models may be used and trained using machine learning. The method includes receiving a request to evaluate an entity with at least two of the plurality of data files. The method includes determining at least one entity interrelated to the entity. The at least one entity can at least in part owned by one or more owners common to the entity. The method includes generating at least two forecasts based on the entity, the at least one entity, and the at least two of the plurality of data files and providing an output.