Predictive Analysis Template Ranking for Forecasting Accuracy
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Solution Overview
Problem
Business organizations face difficulties in applying predictive analytics due to the complexity of various statistical algorithms and their situational applicability, requiring a deep understanding of algorithms and scenarios for accurate forecasting, which most users lack.
Innovation Solution
A computer-implemented method that identifies and ranks applicable templates and algorithms from a library based on dataset analysis, usage history, and input parameters, simplifying the selection and configuration of algorithms for predictive analytics without requiring extensive user knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple statistical algorithms are provided for predictive analytics, then the accuracy and applicability of forecasting improve, but the complexity of system operation increases
Solution Approach 1:
The system performs self-service by automatically evaluating datasets against multiple statistical algorithms, selecting the most appropriate algorithm without requiring user expertise. The computer system autonomously compares dataset characteristics with algorithm requirements and executes the selected algorithm, eliminating the need for users to manually understand or choose from multiple statistical methods while still achieving accurate forecasting results
Solution Approach 2:
The patent introduces an intermediary layer between the user and the statistical algorithms. This intermediary system automatically evaluates datasets, matches them with appropriate algorithms based on predefined criteria, and handles the complexity of algorithm selection. Users interact only with the simplified interface while the intermediary manages the complex algorithm evaluation and selection process in the background
2Measurement precision
If users are provided with deep understanding of statistical algorithms, then the precision of algorithm selection improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary action by pre-evaluating and comparing multiple statistical algorithms against the dataset before user interaction. The computer system automatically assesses dataset characteristics, pre-identifies suitable algorithms, and prepares the optimal selection in advance, so that when users need results, the precise algorithm selection is already completed, eliminating time loss
Solution Approach 2:
The patent replaces the mechanical process of manual algorithm evaluation and selection with an automated computer-based system. Instead of users manually understanding and selecting algorithms (a time-consuming mechanical process), the system automatically performs dataset analysis, algorithm comparison, and selection, achieving both precise algorithm matching and rapid execution without requiring user expertise or time investment
Data Source
AI summary
A technique is described that simplifies the process for applying predictive analysis to a dataset. The technique can recommend multiple templates to a user. Each recommend template contains algorithms which can be applied to the dataset. When a template is selected, the technique can rank the available algorithms of the selected template based on factors such as values in the dataset, characteristics of the dataset, and the usage history of the dataset or the algorithms in prior instances. The technique can automatically select the highest ranked algorithm and apply it to the dataset. In some examples, input parameters used to configure the algorithm can also be automatically selected.