LLM Spike Explanation for Context-Rich Time-Series Analysis
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Solution Overview
Problem
Existing methods for spike analysis in time-series data struggle to provide comprehensive and context-rich explanations, particularly when dealing with unstructured textual data from extensive document repositories, limiting their effectiveness in identifying the underlying causes of data spikes.
Innovation Solution
The use of Large Language Models (LLMs) like GPT to analyze representative documents correlated with identified driving factors, incorporating unstructured text and user query intent, enhances the generation of detailed, contextually informed explanations for spikes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical methods and causal inference techniques are used for spike detection, then spike detection capability is improved, but the ability to provide comprehensive context-rich explanations deteriorates
Solution Approach 1:
The patent combines traditional statistical spike detection methods with large language model-based contextual analysis to create a unified system that delivers both precise spike detection and comprehensive explanations. The system merges structured metric data analysis with unstructured document repository analysis to provide complete insights.
Solution Approach 2:
The patent introduces an intermediary layer that connects spike detection with contextual explanation generation. This intermediary processes detected spikes and queries document repositories to gather relevant contextual information, bridging the gap between detection and explanation.
2Measurement precision
If machine learning models are used for pattern recognition, then pattern detection capability is improved, but the depth of explanatory insights deteriorates
Solution Approach 1:
The patent segments the analysis into distinct components: machine learning-based pattern detection, document retrieval, and explanation generation. Each component specializes in its function, with the LLM generating human-readable explanations that elaborate on the patterns detected by machine learning models.
Solution Approach 2:
The patent adds a new dimension to pattern recognition by incorporating unstructured text analysis from document repositories. This transforms the analysis from purely numerical pattern detection to a multi-dimensional approach that includes contextual, qualitative insights.
3Loss of information
If extensive manual interpretation is performed to elucidate underlying causes, then explanation depth is improved, but analysis time and complexity deteriorates
Solution Approach 1:
The patent enables the system to automatically generate comprehensive explanations without requiring extensive manual interpretation. The large language model autonomously analyzes retrieved documents and synthesizes explanatory insights, eliminating the need for manual domain expertise intervention.
Solution Approach 2:
The patent changes the parameter of explanation generation from manual to automated by utilizing LLM capabilities. This transformation maintains deep explanatory quality while dramatically reducing the time and human resources required for analysis.
4Loss of information
If unstructured textual data from document repositories is analyzed, then contextual understanding is improved, but processing complexity and resource requirements deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing document repositories before spike analysis. This preparation enables efficient retrieval and analysis of relevant documents when spikes occur, reducing processing complexity during actual analysis.
Solution Approach 2:
The patent applies partial action by selectively analyzing only the portions of document repositories that are relevant to detected spikes. Rather than processing entire document sets, the system queries for specific contextual information related to identified spike events.
Data Source
AI summary
This disclosure addresses deficiencies in existing methods for analyzing spikes in time-series data, particularly when dealing with vast document repositories. A method includes receiving a user specification of objects of interest, and by subsequently identifying spikes of mentions of these objects in the documents. The method includes retrieving metric data and context data related to mentions of objects of interest in relevant documents from a repository, both from spikes and other time intervals. By analyzing these documents, it is possible to pinpoint the key factors driving the spikes. Finally, use of an LLM provides capabilities to generate comprehensive explanations. This is achieved by submitting one or more prompts to LLM(s), where the prompts incorporate the specification of the objects of interest (or a reformulation thereof), the identified driving factors and a number of representative documents connected to the key driving factors.