Graph Attention Temporal Network for Sequence Prediction
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Solution Overview
Problem
Existing predictive data analysis solutions face inefficiencies in capturing complex structural correlations and temporal dependencies of classification features in temporal sequences, which affects predictive accuracy and requires extensive computational resources.
Innovation Solution
The use of a graph-attention augmented temporal network that generates representative embeddings for temporal sequences through dynamic co-occurrence graphs and initial embeddings, leveraging a global guidance correlation graph and a tree-based long short-term memory model to improve prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive data analysis methods are used, then computational resources and training data requirements are extensive, but predictive accuracy is reduced due to inability to capture complex structural correlations and temporal dependencies
Solution Approach 1:
The patent segments the classification feature space into hierarchical clusters using graph-based community detection algorithms. This segmentation allows the model to process complex temporal sequences by breaking them into manageable clusters, reducing computational complexity while maintaining the ability to capture structural correlations and temporal dependencies for accurate predictions
Solution Approach 2:
The patent transforms temporal sequence data into a multi-dimensional graph structure where nodes represent classification features and edges represent temporal relationships. This dimensional transformation enables the model to capture complex structural correlations and temporal dependencies in a unified framework, improving predictive accuracy while optimizing resource utilization through graph-based efficient computations
2Measurement precision
If complex models are used to capture structural correlations and temporal dependencies, then predictive accuracy improves, but computational complexity and training requirements increase
Solution Approach 1:
The patent employs dynamic graph structures where the graph topology and edge weights are continuously updated based on temporal sequence data. This dynamic approach allows the model to adaptively capture evolving structural correlations and temporal dependencies, achieving high predictive accuracy while maintaining computational efficiency through incremental updates rather than retraining complex models
Solution Approach 2:
The patent introduces graph-based embeddings as intermediary representations that capture structural correlations and temporal dependencies. These embeddings serve as a bridge between raw temporal data and final predictions, enabling the model to achieve high predictive accuracy while reducing computational complexity by preprocessing complex relationships into compact embedding vectors
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a representative embeddings for a plurality of temporal sequences by using a graph attention augmented temporal network based on dynamic co-occurrence graphs for preceding temporal sequences and initial embeddings, where the dynamic co-occurrence graphs are projections of a global guidance co-occurrence graph on classification features of the preceding temporal sequences, and the initial embeddings are generated by processing a latent representation of corresponding classification features that is generated by a sequential long short term memory model as well as a classification feature tree using a tree-based long short term memory model.