Temporal Cluster Conditioning for Accurate Extrapolation
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Solution Overview
Problem
Raw data, due to its volume, heterogeneity, and complexity, often leads to improperly interpreted predictive models that yield undesired predictions and performance.
Innovation Solution
An apparatus and method that condition raw data through temporal interpolations by clustering it into primary and secondary clusters, assigning temporal interpolations, and generating a progression model based on these clusters to improve data quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw data is used directly for training predictive models, then the volume and heterogeneity of data are preserved, but the prediction accuracy and model performance deteriorate due to improper interpretation
Solution Approach 1:
The patent segments raw data into multiple clusters based on temporal characteristics and patterns. Each cluster represents a distinct temporal regime or behavior type, allowing the model to process heterogeneous data in a structured manner. This segmentation transforms the complex raw data into organized clusters that can be interpreted more effectively, resolving the contradiction between preserving data volume and improving prediction accuracy.
Solution Approach 2:
The patent applies parameter changes by transforming raw temporal data into clustered representations with specific temporal interpolation parameters. This transformation changes the state of the data from unprocessed raw values to structured clustered parameters, enabling better model interpretation while maintaining the essential temporal information, thus improving prediction accuracy without losing data volume.
2Loss of information
If raw data is clustered into multiple primary and secondary clusters, then temporal trends and patterns are revealed, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent implements a two-level clustering segmentation process where raw data is first divided into primary clusters based on broad temporal characteristics, then further segmented into secondary clusters within each primary cluster. This hierarchical segmentation preserves temporal patterns by organizing data at multiple levels of granularity, while the structured approach to segmentation makes the complex process more manageable and systematic.
Solution Approach 2:
The patent introduces additional dimensions to the data structure by creating primary and secondary cluster hierarchies. This dimensional transformation organizes the complex temporal data into a multi-layered structure where each level captures different aspects of temporal patterns, thereby preserving information while providing a systematic framework for handling the complexity.
3Measurement precision
If temporal interpolations are assigned to clusters, then extrapolation accuracy is improved, but the computational processing time and resources increase
Solution Approach 1:
The patent applies preliminary action by assigning temporal interpolations to clusters during the data preprocessing and clustering phase, before the actual predictive modeling and extrapolation tasks. This advance preparation of temporal interpolation parameters for each cluster reduces the computational burden during model training and inference, as the temporal relationships are already established and stored in the clustered structure, thereby improving extrapolation accuracy while reducing processing time during critical operations.
Data Source
AI summary
An apparatus for conditioning raw data based on temporal interpolations to generate optimal extrapolations of an entity, wherein the apparatus comprises at least a processor configured to receive raw data associated with a temporal element from an entity; condition the raw data, wherein conditioning the raw data comprises clustering the raw data into at least two primary clusters; determine an extrapolation for the first primary cluster and the second primary cluster; and generate a progression model as a function of the extrapolations, wherein generating the progression model further comprises ranking the first primary cluster and the second primary cluster as a function of the first temporal interpolation and the second primary interpolation.