Time Series Trend Clustering for Irregular Anomaly Detection
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Solution Overview
Problem
Existing time series algorithms struggle with accurately detecting anomalies in datasets with irregular trends due to non-periodic events, leading to misclassification, false alarms, inefficiency, and inability to handle dynamic and domain-specific patterns, which is critical in environments requiring real-time and precise anomaly detection.
Innovation Solution
A comprehensive system utilizing real-time data logging, extraction, cleanup, feature engineering, clustering, and predictive modeling to identify unique trends and patterns, followed by hierarchical modeling to capture even small irregularities, with automated responses to detected anomalies.
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 non-periodic events
Solution Approach 1:
The patent segments time series data into multiple clusters based on trend influencers (e.g., holidays, weekends, seasonal patterns). Each cluster is analyzed separately with customized models, allowing the system to handle irregular trends effectively while maintaining manageable complexity through modular processing.
Solution Approach 2:
The system dynamically adapts to changing data patterns by automatically identifying trend influencers and adjusting cluster assignments over time. This dynamic approach allows the anomaly detection system to evolve with the data, improving accuracy without requiring complete system redesign when new patterns emerge.
2Reliability
If traditional time series algorithms are applied, then the processing is fast and efficient, but false alarms increase when data contains irregular trends from events like holidays or special promotions
Solution Approach 1:
The patent applies different anomaly detection models and parameters to different data clusters based on their specific characteristics. For example, holiday-related clusters use different thresholds and patterns than regular operational clusters, reducing false alarms by tailoring the detection approach to local data qualities rather than applying a uniform standard.
3Measurement precision
If single predictive models are used, then the model development is straightforward, but the ability to capture complex irregular trends and sub-trends is insufficient
Solution Approach 1:
The patent divides the time series data into multiple clusters based on identified trend influencers, allowing different predictive models to be applied to each cluster. This segmentation enables the system to capture complex irregular trends and sub-trends within each cluster while keeping individual models manageable in complexity.
Solution Approach 2:
The system uses a universal clustering framework that can handle multiple types of trend influencers (holidays, weekends, seasonal patterns, promotional events) through a single unified approach. This multi-functional clustering mechanism captures diverse complex trends without requiring separate specialized models for each type of irregularity.
4Adaptability or versatility
If traditional anomaly detection systems are used, then the implementation is simple, but the adaptability to dynamic environments and domain-specific patterns is poor
Solution Approach 1:
The system dynamically identifies trend influencers and automatically adjusts cluster assignments based on changing data patterns. This dynamic adaptability allows the system to respond to new trends and domain-specific patterns without manual reconfiguration, while the automated nature of the adaptation keeps implementation complexity manageable.
Data Source
AI summary
Time series anomalies with unusual patterns are found. Data collecting from data inception points starts the invention. Real-time data logging ensures temporal integrity and correct sequence analysis. After extracting important data points, the logged data is analyzed for trends and abnormalities. The data is cleaned to reduce noise and unnecessary information for feature engineering accuracy and dependability. For orderly retrieval, primary data sources receive engineered features. Advanced algorithms detect regularities and abnormalities, including holiday and weekend-influenced trends. Each trend cluster receives a predictive model after data clustering. Serializing these models improves storage and retrieval. After classifying new data points to their trend cluster, appropriate models are loaded for comparison to expected patterns. Alerts and corrective measures are automatically triggered by substantial deviations. To detect anomalies accurately, the system continuously monitors and analyzes incoming data.