Adaptive Window Anomaly Detection via Dynamic Ground-Truth Sizing
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Solution Overview
Problem
Current anomaly detection methods typically use fixed-size windows that fail to adapt to data patterns, leading to late detection and potential false positives, as they do not proactively identify anomalies before their occurrence, thereby missing early warning signs and associating non-anomalous data with anomalies.
Innovation Solution
The method involves generating an adaptive ground-truth window based on anomaly detection scores, assigning weighting values to data points within this window, and training a machine learning system using these scores and values to improve the likelihood of early anomaly detection, enabling proactive measures before anomalies occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed-size windows are used for anomaly detection, then the detection process is simple, but the detection accuracy and timing are poor
Solution Approach 1:
The patent implements dynamic window sizing where the ground-truth window adapts its size based on the specific anomaly detection task and data characteristics. Instead of using fixed-size windows, the system dynamically determines the appropriate window size to capture relevant pre-anomaly patterns, thereby improving detection accuracy while maintaining manageable system complexity through adaptive rather than static structures.
Solution Approach 2:
The system changes the parameter of window size from a fixed constant to a dynamic variable that adjusts based on anomaly characteristics, data patterns, and detection requirements. This parameter change enables the detection mechanism to optimize its performance for different scenarios, improving overall accuracy without requiring completely separate detection systems for each case.
2Reliability
If fixed-size windows are used, then the system is easy to implement, but false positives increase
Solution Approach 1:
The dynamic ground-truth window adjusts its size and content based on the specific anomaly being detected, allowing the system to focus on relevant data points and reduce noise. This dynamic adaptation improves reliability by ensuring that the training data contains genuine pre-anomaly patterns rather than random fluctuations, thereby reducing false positives while maintaining reasonable implementation complexity.
Solution Approach 2:
The system applies different window sizes and compositions to different anomalies based on their local characteristics. Each anomaly receives a customized ground-truth window that is optimized for its specific pattern, rather than applying a uniform window size to all cases. This local optimization improves detection reliability and reduces false positives by tailoring the detection approach to each anomaly's unique characteristics.
3Loss of time
If fixed-size windows are used, then the processing is faster, but early anomaly detection is missed
Solution Approach 1:
The dynamic ground-truth window is specifically designed to capture data points that precede an anomaly, enabling the system to detect anomalies before they fully manifest. By incorporating pre-anomaly patterns into the training data, the system can issue early warnings and take preventive actions, reducing the loss of time associated with anomaly response while maintaining efficient processing through targeted rather than exhaustive analysis.
4Measurement precision
If adaptive ground-truth windows are generated, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system employs automated algorithms that self-determine the appropriate ground-truth window size and composition based on the data and anomaly characteristics. Rather than requiring manual configuration or complex external optimization, the system self-adjusts the window parameters, improving detection precision while keeping the added computational complexity manageable through self-service rather than externally-managed complexity.
Data Source
AI summary
Detecting data anomalies by receiving a first data set related to a first variable metric, determining data anomaly detection scores for data points of the first data set according to a plurality of data anomaly detection techniques, generating an adaptive ground-truth window according to the data anomaly detection scores, assigning a weighting value to each data point within the adaptive ground-truth window, training a machine learning system using the set of data anomaly detection scores and weighting values, and providing a trained machine learning system for evaluating a second data set.