Hybrid Clustered Prediction for Node Segmentation
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Solution Overview
Problem
Conventional software solutions for predicting future attributes of nodes in datasets using holistic time-series models are inaccurate and inefficient, requiring heavy computing power and human intervention, and often lead to overfitting due to reliance on all nodes rather than specific node segments.
Innovation Solution
A hybrid method utilizing clustering algorithms to generate and execute prediction models, segmenting nodes into clusters based on attributes, and applying time-series models specific to each cluster to predict future attributes efficiently without excessive computing power or human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a holistic time-series model is generated based on all nodes, then the model can predict overall trends, but the prediction accuracy for specific node segments deteriorates
Solution Approach 1:
The patent segments the set of nodes into multiple clusters based on similarity metrics, where each cluster represents a homogeneous group of nodes with similar characteristics. This segmentation allows the system to generate separate time-series models for each cluster, enabling accurate predictions for specific node segments while maintaining the ability to predict overall trends across all clusters.
2Measurement precision
If time-series models are generated for each individual node, then prediction accuracy for that node improves, but computing power requirements deteriorate
Solution Approach 1:
The patent merges nodes into clusters based on their similarity in attributes and behavior patterns. By combining multiple nodes into a single cluster, the system generates one time-series model per cluster rather than one model per node. This merging approach significantly reduces the total number of models that need to be generated and executed, thereby reducing computing power requirements while still providing accurate predictions for individual nodes through their cluster's model.
3Measurement precision
If time-series models are generated for each individual node, then prediction accuracy may improve, but the risk of overfitting data variations deteriorates
Solution Approach 1:
By merging multiple nodes into clusters, the system aggregates data from multiple sources to train each time-series model. This aggregation increases the amount of training data available for each model, improving its generalization capability and reducing overfitting to noise or variations in individual node data. The clustered approach provides a more robust foundation for predictions by leveraging patterns across multiple nodes rather than relying solely on a single node's historical data.
4Measurement precision
If administrators manually identify time-series models for particular nodes, then prediction accuracy improves, but reliance on subjective skills and human intervention deteriorates
Solution Approach 1:
The patent implements an automated clustering system that autonomously groups nodes into clusters based on similarity metrics and automatically generates appropriate time-series models for each cluster. This self-service approach eliminates the need for administrators to manually identify and assign models to nodes, removing subjective human judgment from the process. The system automatically determines the optimal clustering structure and selects appropriate models based on the data characteristics, achieving both high accuracy and full automation.
Data Source
AI summary
Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. The systems and methods described herein retrieve a set of nodes and execute various clustering algorithms in order to segment the nodes into different clusters. The systems and methods described herein also describe generating one or more prediction models, such as time-series models, for each cluster of nodes. When a node with unknown/limited data and attributes is identified, the methods and systems described herein first identify a cluster most similar the new node, identify a corresponding prediction model, and execute the identified prediction model to calculate future attribute of the new node.