Change Point Detection Using Residual Ratios and Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Constructing a robust and versatile real-time change point detection system for time series data remains challenging due to the need for extensive development and tuning of sophisticated models, which are often domain-specific and not easily reusable or configurable for different data types or programmatic actions.
Innovation Solution
A change point detection system that automatically updates simpler models by predicting change points in input time series data, using methods such as residual metric comparison and feature-based clustering, allowing for quick model updates and configuration, and enabling the use of multiple detection methods in combination for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated change point detection models are used, then detection accuracy is improved, but model complexity and development time increase
Solution Approach 1:
The patent segments the complex change point detection problem into multiple simpler models, each trained on specific data segments identified through clustering. Instead of using one sophisticated model for all scenarios, the system divides the time series data into segments and applies appropriate simpler models to each segment, reducing overall complexity while maintaining detection accuracy.
Solution Approach 2:
The system dynamically changes model parameters based on detected data patterns. When a change point is detected, the system adjusts which simpler model to apply and modifies training parameters accordingly. This allows the system to adapt to different data characteristics without requiring a single complex model to handle all scenarios.
2Measurement precision
If domain-specific models are built, then detection accuracy for specific applications is improved, but reusability and configurability decrease
Solution Approach 1:
The patent creates a universal change point detection framework that can handle multiple domain-specific scenarios. The system uses general-purpose techniques like residual metric comparison and feature-based clustering that can be applied across different data types and domains. The framework automatically adapts to specific applications through configurable parameters and automated model selection, eliminating the need to build separate domain-specific models for each application.
Solution Approach 2:
The system performs self-configuration and automatic model selection based on the characteristics of the input data. Through automated clustering and change point detection, the system identifies appropriate models and parameters without requiring extensive manual tuning for each domain. This self-service capability enables the same framework to serve multiple domains effectively.
3Speed
If real-time change point detection is implemented, then responsiveness to data changes is improved, but processing power and memory usage increase
Solution Approach 1:
The system applies partial monitoring strategies where not all data points are processed with full computational intensity at all times. Instead of continuously applying complex models to every data point, the system uses lighter-weight monitoring and only activates more computationally intensive analysis when change points are suspected or data patterns warrant deeper inspection. This reduces average processing power requirements while maintaining real-time detection capability.
4Reliability
If sophisticated models are tuned and developed, then detection performance is improved, but development time and effort increase
Solution Approach 1:
The patent reuses proven detection patterns and model structures across different applications. Instead of developing entirely new sophisticated models for each domain, the system copies and adapts established simpler models that have been validated in similar contexts. The framework includes pre-configured detection methods like residual metric comparison and clustering techniques that can be deployed immediately with minimal customization, significantly reducing development time while maintaining reliable performance.
Data Source
AI summary
Computer systems and associated methods are disclosed to detect a future change point in time series data used as input to a machine learning model. A forecast for the time series data is generated. In some embodiments, a fitting model is generated from the time series data, and residuals of the fitting model are obtained for respective portions of the data both before and after a potential change point in the future. The change point is determined based on a ratio of residual metrics for the two portions. In some embodiments, data features are extracted from individual segments in the time series data, and the segments are clustered based on their data features. A change point is determined based on a dissimilarity in cluster assignments for segments before and after the point. In some embodiments, when a change point is predicted, an update of the machine learning model is triggered.