Hierarchical Time Series Modeling for Irregular Trend Anomaly Detection
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Solution Overview
Problem
Existing time series algorithms struggle with accurately detecting anomalies in datasets with irregular trends, particularly due to non-periodic events like holidays or weekends, leading to misclassifications, false alarms, inefficiency, and a lack of scalability and adaptability, especially in dynamic environments.
Innovation Solution
A comprehensive system utilizing real-time data logging, extraction, cleanup, feature engineering, clustering, and predictive modeling to identify unique trends and generate automated responses, employing advanced machine learning techniques to handle complex time series data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time series models are used to detect anomalies, then the system is simple to implement, but the accuracy of anomaly detection deteriorates when data exhibits irregular trends due to holidays or special events
Solution Approach 1:
The patent segments time series data into multiple clusters based on trend influencers (e.g., holidays, weekends, special events). Each cluster represents a distinct pattern group, allowing the system to apply specialized models to each segment rather than treating all data uniformly. This segmentation enables accurate handling of irregular trends while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The system dynamically adapts by identifying trend influencers and adjusting clustering assignments based on changing data patterns. The automated model selection process dynamically chooses appropriate predictive models for each cluster based on the identified trends, enabling the system to respond flexibly to irregular patterns without requiring manual reconfiguration.
2Reliability
If traditional time series models assume consistent trends, then the model structure is simple, but the reliability of anomaly detection deteriorates when unplanned events or sudden pattern changes occur
Solution Approach 1:
The patent divides the time series data into multiple clusters based on identified trend influencers. Each cluster captures a specific pattern type (e.g., holiday patterns, weekend patterns, normal business patterns), allowing the system to reliably detect anomalies within each segment while accommodating overall data complexity through structured segmentation.
Solution Approach 2:
The system changes the parameter of trend assumption from 'consistent' to 'variable by cluster'. By allowing different clusters to have different trend characteristics influenced by specific factors (holidays, events, weekends), the system achieves reliable anomaly detection across diverse patterns without requiring a single complex model.
3Measurement precision
If a single predictive model is used for all data, then the system is computationally efficient, but the precision of anomaly detection deteriorates when multiple overlapping trends are present
Solution Approach 1:
The patent segments data into clusters based on trend influencers, allowing specialized predictive models to be applied to each segment. This segmentation improves precision by capturing nuanced patterns within each cluster while maintaining efficiency through automated model selection and parallel processing of multiple clusters.
Solution Approach 2:
The system applies local quality by using different predictive models for different data clusters based on their specific characteristics. Each cluster receives a model tailored to its pattern type (e.g., seasonal, trending, stationary), improving detection precision locally while the overall system maintains efficiency through automated model assignment.
4Adaptability or versatility
If traditional models do not account for event-specific weightage, then the model is simple to maintain, but the adaptability to dynamic environments deteriorates when data patterns change frequently
Solution Approach 1:
The system achieves adaptability by dynamically identifying trend influencers and reassigning data points to appropriate clusters based on current patterns. The automated model selection process adapts to changing data characteristics without manual intervention, allowing the system to respond to dynamic environments while maintaining manageable complexity through systematic adaptation rules.
Solution Approach 2:
The system incorporates feedback mechanisms where anomaly detection results and pattern recognition continuously inform cluster assignments and model selections. This feedback loop enables the system to adapt to changing patterns over time, improving versatility while maintaining systematic complexity management through automated adjustment based on performance feedback.
Data Source
AI summary
Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.