Cross-Geographical Predictive Data Analysis Using Simulated Inputs
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Solution Overview
Problem
Existing predictive data analysis systems face inefficiencies and reliability issues due to the large amount of data required for cross-geographical predictions, leading to increased network transmission demands and potential data loss.
Innovation Solution
The method involves generating simulated predictive input data for hierarchically inferior geographic domains using superior domain data, employing techniques like Gibbs-sampling-based Markov Chain Monte Carlo routines and zero-inflated Poisson models to reduce data transmission and improve forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If observed data from multiple hierarchically inferior geographic domains is collected for predictive analysis, then prediction accuracy improves, but network transmission demands and data loss risks increase
Solution Approach 1:
The patent extracts and utilizes observed data from hierarchically superior geographic domains to substitute for observed data from hierarchically inferior geographic domains. This extraction approach allows the system to perform predictive analysis with reduced data transmission requirements, as simulated data generated from superior domain observations replaces the need to transmit large volumes of inferior domain data across networks.
2Measurement precision
If observed data from multiple hierarchically inferior geographic domains is collected for predictive analysis, then prediction accuracy improves, but data loss risks increase
Solution Approach 1:
The patent creates simulated copies of inferior domain data by generating synthetic datasets based on observed superior domain data and statistical models. These simulated data copies preserve the statistical properties and patterns needed for accurate prediction while eliminating the risks associated with transmitting and storing large volumes of actual observed data from multiple inferior domains.
3Loss of energy
If simulated predictive input data is generated using superior domain data, then data transmission needs decrease, but data generation complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and analyzing observed superior domain data to establish statistical models and patterns before simulation. This preliminary analysis includes determining appropriate probability distributions (such as zero-inflated Poisson models for count data) and parameter relationships, which are then used to efficiently generate simulated inferior domain data without requiring complex real-time processing during the prediction phase.
Data Source
AI summary
There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing cross-geographical predictive data analysis that enhance network transmission efficiency. In one example, a method includes determining forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period; determining forecasted inferior domain event data for each hierarchically inferior geographic domain associated with the hierarchically superior geographic domain at the forecasting period; determining confirmed inferior domain event data based at least in part on each hierarchically inferior geographic domain; and performing prediction-based actions based at least in part on each confirmed inferior domain event data.