Spatial Temporal Memory Anomaly Detection
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Solution Overview
Problem
Existing predictive models are imperfect, leading to deviations between predicted and actual values or states, which can indicate critical anomalies but are often difficult to detect and analyze in real-time due to the complexity of temporal sequences and spatial patterns in data.
Innovation Solution
A spatial and temporal memory system that learns and detects anomalies by comparing prediction outputs with actual values over time, using a hierarchical structure with spatial poolers and sequence processors to identify discrepancies and associate them with previous system states for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive models are used to make predictions, then prediction capability is improved, but deviation from actual values occurs indicating undetected anomalies
Solution Approach 1:
The system implements feedback by continuously comparing predicted values with actual observed values and using this discrepancy information to detect anomalies. The anomaly detection mechanism feeds back into the predictive system to identify when predictions deviate significantly from reality, allowing for correction and improvement of prediction models.
Solution Approach 2:
The system performs preliminary anomaly detection by monitoring prediction deviations before they become critical failures. By establishing thresholds for acceptable prediction accuracy and continuously checking against these thresholds, the system can identify and flag potential issues in advance, allowing preventive actions to be taken.
2Difficulty of detecting and measuring
If anomaly detection is implemented by comparing predictions with actual values, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary anomaly detection layer that sits between the predictive model and the actual data. This intermediary component handles the complex task of comparing predictions with actual values, calculating deviations, and determining whether anomalies exist, thereby isolating the complexity from the core predictive function.
Solution Approach 2:
The anomaly detection system is segmented into distinct functional components: prediction generation, actual value acquisition, deviation calculation, threshold comparison, and anomaly flagging. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex detection process into manageable steps.
3Measurement precision
If predictive models are made more accurate to reduce deviation, then prediction accuracy is improved, but model complexity and resource requirements increase
Solution Approach 1:
Instead of making the predictive model perfectly accurate (excessive action), the system adopts partial action by accepting a certain level of prediction error as tolerable. The anomaly detection mechanism is designed to identify when deviations exceed acceptable thresholds, allowing the system to maintain simpler, more resource-efficient models while still detecting significant anomalies.
Data Source
AI summary
Detecting patterns and sequences associated with an anomaly in predictions made a predictive system. The predictive system makes predictions by learning spatial patterns and temporal sequences in an input data that change over time. As the input data is received, the predictive system generates a series of predictions based on the input data. Each prediction is compared with corresponding actual value or state. If the prediction does not match or deviates significantly from the actual value or state, an anomaly is identified for further analysis. A corresponding state or a series of states of the predictive system before or at the time of prediction are associated with the anomaly and stored. The anomaly can be detected by monitoring whether the predictive system is placed in the state or states that is the same or similar to the stored state or states.