Time-Stamped Data Event Modeling via Statistical GUI Properties
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Solution Overview
Problem
Accurately modeling time-stamped data to improve predictive ability is complicated due to the varied effects of events such as advertising campaigns, natural disasters, and policy changes, which require complex handling of event characteristics and durations.
Innovation Solution
Systems and methods using graphical user interfaces (GUIs) to define and associate statistical properties of events with time-stamped data, enabling a time series analysis program to generate a mathematical model that predicts future occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex event characteristics and durations are manually modeled to improve predictive accuracy, then model accuracy improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The system automatically detects and models events by analyzing changes in time-stamped data patterns. The event detection algorithm autonomously identifies event boundaries, characteristics, and statistical properties without requiring manual configuration, allowing the system to self-configure complex event models while maintaining high accuracy
Solution Approach 2:
The patent replaces manual mechanical modeling processes with automated computational algorithms. Instead of manually defining event characteristics and durations, the system uses statistical analysis and pattern recognition algorithms to automatically detect and model events, substituting complex manual operations with automated computational processes
2Measurement precision
If manual configuration of event properties is used to achieve accurate modeling, then model accuracy improves, but time consumption and productivity decrease
Solution Approach 1:
The system performs preliminary automated detection and modeling of events before analysis is needed. By continuously monitoring time-stamped data and pre-identifying event patterns, the system prepares event models in advance, eliminating the need for time-consuming manual configuration when analysis is required
Solution Approach 2:
The automated event detection system continuously self-updates event models by analyzing incoming data streams, automatically adjusting event characteristics and durations without human intervention. This self-service approach maintains accurate models while minimizing time consumption
3Reliability
If detailed event characteristics are tracked to improve predictive ability, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential event characteristics and statistical properties needed for prediction from the full time-stamped data set. By isolating and modeling only the relevant event features (such as event type, duration, and impact magnitude) while filtering out extraneous data, the system maintains high predictive ability while reducing data processing complexity
Data Source
AI summary
Systems and methods are provided for handling time-stamped data. The one or more GUIs are used to define properties of an event and to associate the event with time-stamped data. The properties of the event defined using the one or more GUIs includes one or more statistical properties that indicate how the event statistically affects the time-stamped data. A time series analysis program uses the time-stamped data and the associated event to generate a mathematical model.