Knowledge Graph Regularization for Financial Time Series Forecasting
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Solution Overview
Problem
Challenges exist in integrating knowledge graphs with financial time series data for machine learning due to format differences, noisy relationships, scalability issues, and limited data samples, leading to poor prediction performance and overfitting in high-dimensional financial networks.
Innovation Solution
A system that integrates knowledge graphs with time series data by using sparsity-inducing regularizers and graph convolutional networks to guide the learning of forecasting models, leveraging connectivity information from knowledge graphs to improve model training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If knowledge graphs are integrated with financial time series data for machine learning, then the model can leverage connectivity information and relationships among entities, but the model suffers from overfitting and poor prediction performance due to noisy relationships and limited data samples
Solution Approach 1:
The patent applies parameter changes by transforming the knowledge graph structure into an inverse covariance matrix of a Gaussian error term. This transformation changes the parameter representation from graph edges to statistical parameters that can be directly integrated into the machine learning model, allowing the model to leverage connectivity information while maintaining mathematical tractability and reducing overfitting through regularization.
Solution Approach 2:
The patent uses an intermediary approach by introducing a Gaussian error term as a mediator between the knowledge graph structure and the machine learning model. The knowledge graph is transformed into this error term structure, which then serves as a regularizer in the loss function, bridging the gap between relational data and predictive modeling while addressing the noisy relationships issue.
2Productivity
If the machine learning model integrates knowledge graph structure as an error term, then the training efficiency improves, but the model requires sparsity regularization to handle the high-dimensional financial networks
Solution Approach 1:
The patent applies parameter changes by transforming the knowledge graph adjacency matrix into an inverse covariance matrix structure. This parameter transformation enables the model to incorporate relational information efficiently during training while the sparsity regularization controls the complexity by enforcing a sparse structure on the learned parameters, balancing training efficiency with model simplicity.
3Quantity of substance
If the model processes large numbers of entities in the knowledge graph, then the coverage of financial relationships increases, but scalability becomes problematic due to the computational burden
Solution Approach 1:
The patent addresses scalability by changing the parameter representation from dense graph structures to sparse inverse covariance matrices. This parameter transformation reduces the computational burden while maintaining the ability to process large numbers of entities, as the sparsity structure allows for efficient matrix operations and reduced memory requirements.
Data Source
AI summary
Training a machine learning model can include receiving time series data. A knowledge graph structure can be received including nodes and edges, the nodes representing entities associated with the time series data, the edges representing relationships between the nodes connected by the edges. A machine learning model can be structured to forecast a prediction using the time series data. The machine learning model can be structured to integrate the knowledge graph structure as an error term in the machine learning model. The machine learning model can be trained to forecast the prediction based on the time series data and the knowledge graph structure. The error term representing the knowledge graph structure can be regularized for sparsity during training.