Extensible Prediction Model Interface for Confidential Forecasting
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Solution Overview
Problem
Existing data planning and forecasting systems face challenges in accommodating diverse departments and sub-departments with numerous dependencies, requiring sophisticated data storage, modeling, and prediction models, while maintaining confidentiality and flexibility.
Innovation Solution
An extensible software tool that allows loading customizable trained machine learning models, enabling both built-in and externally trained prediction models to generate forecasts, while preserving confidentiality of proprietary data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated data storage and modeling techniques are implemented to handle complex organizational scenarios, then forecasting accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex forecasting functionality into separate, interchangeable prediction model components. Each model (e.g., regression models, time-series models, machine learning models) is an independent module that can be selected and applied to specific data subsets, reducing overall system complexity while maintaining high forecasting accuracy through specialized models.
Solution Approach 2:
The system allows dynamic changing of model parameters and configuration settings without requiring system redesign. Users can adjust complexity parameters, select different model types, and modify data processing parameters to optimize forecasting accuracy for different organizational scenarios, thereby managing system complexity flexibly.
2Adaptability or versatility
If multiple different prediction models are integrated into the software system, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements a universal prediction model interface that can accommodate multiple different prediction models (regression, time-series, machine learning, etc.) through a common architecture. This multi-functional design allows the same software system to handle various model types without proportionally increasing complexity, as all models interact with the system through standardized mechanisms.
Solution Approach 2:
The system introduces intermediary components such as model selection algorithms, automated model comparison tools, and standardized interface layers that mediate between the user and the diverse prediction models. These intermediaries simplify the integration of multiple models by providing uniform access points and automated decision-making mechanisms.
3Measurement precision
If sensitive data is used for training prediction models, then prediction accuracy is improved, but data security risks increase
Solution Approach 1:
The system extracts only the necessary predictive patterns and parameters from sensitive data during the model training phase, while the actual sensitive data remains secured and is not stored or exposed in the prediction system. This extraction approach allows the model to learn from sensitive data without requiring the system to retain or process the raw sensitive information, thereby reducing security risks.
Data Source
AI summary
Systems and methods are provided for performing customizable machine prediction using an extensible software tool. A specification including features of a trained machine learning model can be received and an interface for the trained machine learning model can be generated. The trained machine learning model can be loaded using the interface, the loaded machine learning model including a binary file configured to receive data as input and generate prediction data as output. Predictions can be generated using observed data that is stored according to a multidimensional data model, wherein a portion of the observed data is input to the loaded machine learning model to generate first data predictions, and a portion of the observed data is used by a generic forecast model to generate second data predictions. The first and second data predictions can be displayed in a user interface configured to display intersections of the multidimensional data model.