Forecasting Interface System for Rapid Model Selection
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Solution Overview
Problem
Current forecasting methods in the retail industry and other sectors are time-consuming, often taking months to years to produce accurate forecasts, which can significantly impact costs and revenue due to the complexity of considering numerous variables and large ranges of inputs.
Innovation Solution
A system that enables wide access to forecasting by utilizing a database of preconfigured forecast models, a forecasting interface system, and a scheduler to quickly select and run relevant models based on request data, allowing for quick turnaround of results and efficient business decision-making across various areas such as retail, shipping, and marketing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used to consider numerous variables and large ranges of inputs, then forecast accuracy is improved, but forecasting time increases to months or years
Solution Approach 1:
The system pre-configures multiple forecast models with different variables and parameters before they are needed. When a forecasting request is received, the system can immediately select and execute the appropriate pre-configured model without needing to build the forecasting framework from scratch, thus reducing forecasting time from months to minutes while maintaining accuracy
Solution Approach 2:
The system maintains a database of forecast models with varying parameters, variables, and configurations. By selecting models with appropriate parameter sets based on the specific forecasting needs, the system can quickly adapt to different scenarios without reconfiguring entire models, enabling both accuracy and speed
2Productivity
If multiple forecast models are maintained in a database for quick selection, then productivity is improved, but device complexity increases
Solution Approach 1:
The system creates a universal forecasting platform that can handle multiple types of forecasting requests through a common interface and standardized model structure. This multi-functionality allows the system to serve diverse forecasting needs while managing complexity through standardization and reuse of core components
Solution Approach 2:
The system introduces an intermediary layer (the forecasting interface system) that sits between the user and the complex database of forecast models. This intermediary handles model selection, parameter matching, and execution coordination, shielding users from complexity while enabling quick access to appropriate models
Data Source
AI summary
In some embodiments, apparatuses and methods are provided to enable wide access to numerous different previously compiled forecast modeling. In some embodiments, a system is provided that enables wide access to forecasting, comprising: a forecast model database that maintains numerous different forecast models that when run produce resulting forecast data relevant to making business decisions; and a forecasting interface system configured to receive multiple different forecast requests for forecast request data, which comprises a forecast model index comprising identifiers of the numerous different predefined forecast models and for each of the numerous different forecast models relevance characteristics, wherein the forecasting interface system selects, for each received forecast request, a forecast model of the numerous different forecast models based on a relationship between the corresponding forecast request data and the relevance characteristics.