Time Series Anomaly Detection With Automatic Model Selection
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Solution Overview
Problem
Current methods for selecting anomaly detection models in time series data are inefficient and lead to inaccurate results, particularly in applications requiring fast or near real-time responses, such as cybersecurity and fraud detection, due to manual or brute force approaches that are time-consuming and resource-intensive.
Innovation Solution
A model selection system automatically selects an appropriate anomaly detection model based on temporal traits of the time series dataset, adjusting model parameters and execution settings to improve accuracy and speed, and generates alerts for detected anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual model selection approach is used, then model accuracy may be improved through expert judgment, but time consumption and resource consumption increase significantly
Solution Approach 1:
The system enables self-service by automatically selecting appropriate anomaly detection models based on temporal traits of time series data without requiring manual expert intervention. The automated model selection system analyzes data characteristics and selects models autonomously, eliminating the time-consuming manual exploration process while maintaining detection accuracy.
Solution Approach 2:
The system changes parameters by automatically adjusting model selection based on detected temporal traits (trend, seasonality, heteroskedasticity) in the time series data. This dynamic parameter adjustment allows the system to adapt model choices to data characteristics, achieving high accuracy without manual intervention.
2Reliability
If brute force approach is used to run data through every model, then comprehensive model evaluation is achieved, but computational resources and time requirements become unacceptable
Solution Approach 1:
The system segments the model evaluation process by first identifying temporal traits of the data and then selecting only the relevant models for those traits. This segmentation avoids running data through all available models, significantly reducing computational resources and time while maintaining reliable model selection through targeted evaluation.
Solution Approach 2:
The system performs preliminary action by detecting temporal traits and selecting appropriate models before actual anomaly detection. This preliminary model selection based on data characteristics prevents the need for exhaustive brute force evaluation, achieving both reliability and productivity.
3Measurement precision
If multiple models are evaluated manually, then accurate model selection is achieved, but the process becomes unacceptable for fast or near real-time applications
Solution Approach 1:
The system replaces the mechanical manual model selection process with an automated computational system that detects temporal traits and selects models algorithmically. This substitution enables fast or near real-time model selection while maintaining accuracy through systematic analysis of data characteristics.
Solution Approach 2:
The system introduces an intermediary automated model selection layer between data input and anomaly detection. This intermediary automatically matches temporal traits with appropriate models, enabling fast response speeds while preserving selection accuracy through systematic trait-based matching.
4Productivity
If automated model selection is implemented, then processing speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a general automated model selection framework that handles multiple temporal traits (trend, seasonality, heteroskedasticity) and selects from multiple models using a unified approach. This multi-functional system improves processing efficiency across different data types while managing complexity through a standardized selection mechanism.
Data Source
AI summary
Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.