Time Series Prediction Model Using Instability Feedback
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Solution Overview
Problem
Conventional methods for predicting future states from time series data lack reliability, failing to account for the complexities of sequentiality, complexity, and time series trends, and do not provide sufficient certainty in prediction results, especially in healthcare applications where decision-making depends on accurate forecasts.
Innovation Solution
A method and apparatus that preprocess past and current state data to create a trained model, reflecting instability and time series features, using deep learning to predict future states with high reliability by calculating instability and variation rates, and applying complexity distribution sampling to ensure accurate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional prediction techniques are used, then the system can provide prediction basis for understanding results, but the prediction results lack reliability and certainty
Solution Approach 1:
The patent implements feedback by calculating instability values that measure the deviation between predicted future states and actual observed states. This instability metric is fed back into the prediction model to continuously refine and adjust predictions, thereby improving reliability while providing quantitative certainty measures for decision-making
Solution Approach 2:
The patent replaces conventional statistical prediction methods with an AI-based deep learning model that processes time series data. This substitution enables the system to capture complex temporal patterns and provide more reliable predictions with quantifiable uncertainty measures, addressing the limitation of conventional techniques
2Adaptability or versatility
If time series data with varying lengths is analyzed, then the system can handle different patient histories, but the discrepancy in time series length complicates the analysis
Solution Approach 1:
The patent applies dynamics by designing a prediction model that adapts to varying time series lengths through dynamic feature extraction and temporal pattern recognition. The deep learning model dynamically adjusts to different patient histories by learning from variable-length sequences, maintaining versatility while managing analysis complexity through automated temporal feature processing
3Reliability
If multiple features including numerical and categorical data are analyzed, then the system can capture complex patient characteristics, but the data complexity increases significantly
Solution Approach 1:
The patent segments complex patient data into distinct numerical and categorical feature sets, processing each type through specialized preprocessing pipelines. This segmentation allows the system to capture complex patient characteristics reliably while managing data complexity through structured, modular processing approaches that handle different data types appropriately
Data Source
AI summary
Disclosed herein is a method and apparatus for predicting a future state and reliability based on time series data. In the method and the apparatus, a future state is predicted by preprocessing past state data and executing an algorithm based on the preprocessed past state data to generate a trained model, followed by preprocessing current state data and executing an algorithm based on the created trained model, the preprocessed current state data, and the preprocessed past state data.