Topological Hierarchical Forecasting for Demand Relationship Detection
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Solution Overview
Problem
Existing methods for analyzing large multidimensional datasets are insufficient in identifying important relationships and often lose detail due to sensitivity to large-scale distances, leading to noise and loss of inherent relationships.
Innovation Solution
A method involving topological hierarchical decomposition is employed to generate explainable insights, using past and future customer attention matrices and self-attention arrays to forecast demand, with features like past and future directed graph adjacency arrays and customer attention matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional clustering or linear algebraic methods are used to analyze large multidimensional datasets, then the analysis process is simplified, but important relationships are lost and detail is reduced due to sensitivity to large-scale distances
Solution Approach 1:
The patent applies segmentation by dividing the large multidimensional dataset into multiple clusters of varying sizes using a tree-based clustering approach. Instead of treating all data points uniformly, the method segments them into hierarchical groups, allowing relationships within smaller clusters to be preserved while managing the complexity of large-scale data through organized division.
Solution Approach 2:
The patent transforms the analysis from traditional Euclidean distance-based methods to a tree-structure-based dimensional framework. By representing data relationships through tree hierarchies rather than direct spatial distances, the method captures intricate relationships that would be lost in conventional dimensional analyses sensitive to scale.
2Use of energy by moving object
If conventional analysis methods are applied to identify relationships in large datasets, then computational resources are conserved, but the ability to reveal important relationships and patterns is insufficient
Solution Approach 1:
The tree-based clustering method segments the computational task into hierarchical levels, where data is organized into clusters of clusters. This segmentation allows the system to identify important relationships at multiple scales without requiring exhaustive computation across all data points simultaneously, balancing computational efficiency with relationship detection capability.
Solution Approach 2:
The patent performs preliminary organization of data into tree-based clusters before conducting detailed analysis. By pre-structuring the data hierarchy, the method prepares the dataset in a way that enables efficient subsequent analysis, reducing the computational burden while maintaining the ability to reveal important relationships during the analysis phase.
3Loss of information
If detailed analysis of large datasets is performed to preserve all information, then relationship detail is maintained, but the analysis becomes too sensitive to large-scale distances and creates noise
Solution Approach 1:
The patent segments the dataset into hierarchical clusters, allowing detailed analysis to be applied within smaller, more manageable clusters rather than across the entire large dataset. This segmentation isolates local relationships from the noise of large-scale distances, preserving relationship detail while eliminating the harmful sensitivity to global scale variations.
Solution Approach 2:
The method applies different analysis qualities to different parts of the data structure. Within each tree-based cluster, detailed local analysis is performed to preserve relationship detail, while the overall hierarchical structure provides a framework that filters out noise from large-scale distances. Each cluster can be analyzed with appropriate detail without being affected by distant data points.
Data Source
AI summary
An example computer-implemented method for temporal data analysis and forecasting utilizes topological hierarchical decompositions to process historical and future time windows. The method receives temporal data and generates multiple sets of historical time subsets with varying lengths, where information in shorter subsets is duplicated in longer ones. Future time windows are also generated in a similar manner. Future time windows are chronologically after a given initial time. The method creates past and future topological hierarchical decompositions and directed graph adjacency arrays. Customer attention matrices are generated for past and future windows, and matrix multiplications are performed to create self-attention arrays. These arrays are then multiplied together. The method culminates in providing a dashboard for forecasting demand after an initial time point, enabling comprehensive temporal data analysis and prediction.