Anomaly Detection via Iterative Model Refinement
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Solution Overview
Problem
Manual anomaly detection in training data sets for predictive models is labor-intensive and error-prone, especially with large datasets, and conventional methods struggle with scalability and handling natural data variations.
Innovation Solution
An automated anomaly detection system that optimizes training data sets by removing anomalies and allows user feedback to correct false detections, using a combination of algorithms and user input to iteratively refine the data set and improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual anomaly detection is used to ensure accurate training data, then data quality is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs self-service anomaly detection by automatically analyzing training data using statistical methods and machine learning models to identify anomalies without requiring manual review, thereby eliminating time-consuming human annotation while maintaining detection accuracy
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system that uses algorithms and machine learning models to detect anomalies, substituting human labor with an electronic detection mechanism that operates faster and without fatigue
2Reliability
If manual anomaly labeling is performed to remove errors from training data, then model accuracy is improved, but error rates in labeling increase due to human fatigue
Solution Approach 1:
The system autonomously detects and labels anomalies using programmed algorithms and machine learning models, eliminating human fatigue and inconsistency while maintaining high labeling accuracy through automated statistical analysis and pattern recognition
Solution Approach 2:
The system incorporates feedback mechanisms where detected anomalies are validated and refined through iterative processing, allowing the model to learn from its detections and improve labeling accuracy over time without human intervention
3Reliability
If extensive manual review is conducted to detect all anomalies in large datasets, then detection completeness is improved, but productivity decreases due to labor intensity
Solution Approach 1:
The patent replaces manual data processing with automated computational systems that can analyze large datasets at high speed using parallel processing and efficient algorithms, maintaining detection completeness while increasing productivity by orders of magnitude
Solution Approach 2:
The system segments the large dataset into manageable chunks and processes them through distributed computational resources, enabling comprehensive anomaly detection across the entire dataset while maintaining high processing throughput through parallel execution
4Productivity
If automated anomaly detection is implemented to increase processing speed, then productivity is improved, but false anomaly detections increase
Solution Approach 1:
The system uses feedback loops where initial anomaly detections are validated against multiple criteria and thresholds, with results fed back into the model for iterative refinement, reducing false positives while maintaining high detection speed through efficient validation processes
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on data characteristics and performance metrics, optimizing the balance between detection speed and accuracy by modifying sensitivity levels and confidence requirements for anomaly classification
Data Source
AI summary
Embodiments of the present technology provide systems, methods, and computer storage media for facilitating anomaly detection. In some embodiments, a prediction model is generated using a training data set. The prediction model is used to predict an expected value for a latest (current) timestamp, which is used to determine that the incoming observed data value is an anomaly. Based on the incoming observed data value determined to be the anomaly or not, a corrected data value is generated to be included in the training data set. Thereafter, the training data set having the corrected data value is used to update the prediction model for use in determining whether a subsequent observed data value is anomalous. Such a process may be performed in an iterative manner to maintain optimized training data and prediction model.