ML Model Selection for Time Series Anomaly Detection
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Solution Overview
Problem
Current anomaly detection methods for time series data in network monitoring systems, such as threshold-based and statistic-based approaches, are inefficient due to manual configuration, lack of adaptability to data pattern changes, and assumptions of normal distribution, which do not account for seasonality in time series data.
Innovation Solution
A system that selects and evaluates machine learning models to generate clusters of time series data, using algorithms like K-means and Gaussian Mixture Models, to automatically determine anomaly detection thresholds and improve prediction accuracy, thereby reducing manual configuration and enhancing adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If threshold-based anomaly detection methods are used, then anomaly detection can be performed, but manual configuration is required and the method does not adapt to data pattern changes
Solution Approach 1:
The system automatically selects machine learning models and configures anomaly detection thresholds without manual intervention. The model selection module autonomously evaluates multiple ML models, selects the most suitable one based on data characteristics, and configures thresholds adaptively, eliminating the need for manual trial-and-error configuration while maintaining high adaptability to data pattern changes.
Solution Approach 2:
The system dynamically changes parameters by selecting different machine learning models and their associated parameters based on the characteristics of the time series data. Instead of using fixed threshold parameters, the system adapts parameters automatically through model selection and training, allowing the anomaly detection mechanism to respond to changing data patterns without manual reconfiguration.
2Reliability
If statistic-based anomaly detection methods are used, then anomaly detection can be performed, but the method requires long history data and assumes normal distribution which does not account for seasonality
Solution Approach 1:
The system employs dynamic model selection that adapts to the temporal characteristics of time series data. Different machine learning models are selected based on the specific patterns and seasonality present in the data, rather than using a static statistical method. The system can dynamically switch between models and adjust their parameters to handle seasonal patterns and non-normal distributions effectively.
Solution Approach 2:
The system changes the underlying detection parameters by selecting appropriate machine learning models that are suited for different data distributions and seasonal patterns. Instead of relying on normal distribution assumptions, the system uses models like Prophet and LSTM that can naturally handle seasonality and various data distributions, improving reliability without requiring long historical data periods.
3Measurement precision
If multiple machine learning models are evaluated for each cluster, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the time series data into multiple clusters based on similarity metrics, and then evaluates machine learning models only for representative samples from each cluster. This segmentation approach allows the system to assess model performance across diverse data patterns without evaluating every single time series, thereby maintaining high prediction accuracy while reducing computational complexity through representative sampling.
Solution Approach 2:
The system performs partial evaluation by selecting a subset of representative time series from each cluster to evaluate machine learning models, rather than exhaustively evaluating all data points. This partial action approach provides sufficient accuracy for model selection while significantly reducing the computational burden, as the selected representatives capture the essential characteristics of each cluster.
Data Source
AI summary
A method for machine learning model selection for time series data is disclosed. Sets of time series data is obtained. The time series data is clustered using a clustering algorithm. A similarity value of the clusters is evaluated and a quantity of clusters is selected. Machine learning models are evaluated using a center of each cluster of time series data. A machine learning model is selected for each cluster. Selection may be updated.