Real-Time Anomaly Detection Using State-Space Representation
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Solution Overview
Problem
Current anomaly detection systems face challenges in real-time processing of large volumes of time-series data from complex domains like data center management, where identifying anomalies is crucial to prevent malfunctions and resource misallocation, and existing methods struggle with dealing with sparse high-dimensional state spaces and unknown states.
Innovation Solution
A framework for real-time anomaly detection using a state-space representation and supervised learning techniques to identify anomalies by associating sequences of states with annotated anomalies, allowing for probabilistic causality modeling and distinguishing between known and unknown behaviors without requiring explicit knowledge of all states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection methods are used to process large volumes of time-series data, then detection coverage can be achieved, but real-time processing efficiency deteriorates due to computational complexity
Solution Approach 1:
The patent segments the time-series data processing into distinct phases: offline model training using historical data, and online real-time inference using the pre-trained model. This segmentation allows computationally intensive operations to be performed offline, while online processing uses lightweight operations that maintain real-time performance.
Solution Approach 2:
The system performs preliminary actions by training anomaly detection models offline using historical time-series data and state-space representations. This pre-training enables the system to quickly classify new data points in real-time without repeating the heavy computational work of model training, thus improving processing speed while maintaining detection accuracy.
2Measurement precision
If comprehensive state-space representation is created to cover all possible states, then detection precision improves, but system complexity increases due to handling sparse high-dimensional spaces
Solution Approach 1:
The patent creates simplified copies or representations of the complex state-space through clustering algorithms that group similar states together. Instead of handling every possible high-dimensional state individually, the system creates a manageable number of state clusters that capture the essential characteristics, reducing complexity while preserving detection precision.
Solution Approach 2:
The system transforms the complex high-dimensional state-space into a more manageable representation by changing parameters through dimensionality reduction techniques and clustering. This allows the system to work with condensed state representations that maintain the necessary information for accurate anomaly detection without the computational burden of the full high-dimensional space.
3Reliability
If the system attempts to classify all possible states including unknown states, then detection completeness improves, but processing time increases due to exploration of unknown state space
Solution Approach 1:
The system implements self-service through incremental learning mechanisms that automatically adapt to unknown states without requiring manual intervention or extensive exploration. When new unknown states are encountered, the system automatically updates its model in an incremental manner, allowing it to handle unknown states efficiently while maintaining detection completeness.
Solution Approach 2:
The system prepares for unknown states by incorporating incremental learning capabilities in advance. This preliminary action enables the system to quickly adapt to new states when they occur, rather than spending excessive time exploring and classifying them from scratch, thus maintaining both completeness and efficiency.
4Measurement precision
If historical data is extensively used to train anomaly detection models, then model accuracy improves, but adaptation speed to new states deteriorates
Solution Approach 1:
The patent implements dynamic model updating through incremental learning, where the anomaly detection models are continuously adapted to new data as it arrives. This dynamic approach allows the system to maintain high accuracy from historical training data while simultaneously adapting to new states and patterns, balancing accuracy and adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where the performance on new data is continuously monitored and used to trigger incremental retraining or model updates. This feedback loop ensures that the system maintains high accuracy from historical training while adapting quickly to new states when changes are detected, resolving the contradiction between accuracy and adaptability.
Data Source
AI summary
Methods and apparatus are provided for real-time anomaly detection over sets of time-series data. One method comprises: obtaining a state-space representation of a plurality of states and transitions between said states based on sets of historical time-series data; obtaining an anomaly detection model trained using a supervised learning technique, wherein the anomaly detection model associates sequences of states in the state-space representation with annotated anomalies in the sets of historical time-series data and assigns a probability to said sequences of states; and, for incoming real-time time-series data, determining a likelihood of a current state belonging to a plurality of possible states in the state-space representation; and determining a probability of incurring said annotated anomalies based on a plurality of likely current state sequences that satisfy a predefined likelihood criteria. Anomalous behavior is optionally distinguished from previously unknown behavior based on a predefined likelihood threshold.