Time Series Decomposition for Anomaly Detection
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Solution Overview
Problem
Current anomaly detection and predictive modeling techniques for time series data in online services often fail to accurately account for seasonal, level, and spike components, leading to inaccurate forecasts and missed anomalies due to assumptions about the absence or negligible contribution of these latent components.
Innovation Solution
An analytical system decomposes time series data into seasonal, level, and spike components using an optimization algorithm that minimizes an objective function, allowing for automated extraction of these components without relying on human input or assumptions about their presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated algorithms are used to analyze time series data, then productivity is improved, but measurement precision deteriorates due to inability to account for latent components
Solution Approach 1:
The patent segments the time series data into distinct latent components (seasonal, level, spike, and error components) using decomposition algorithms. This segmentation allows automated algorithms to process each component separately with appropriate methods, maintaining both automation and precision in anomaly detection and forecasting.
Solution Approach 2:
The patent introduces intermediate processing steps that automatically identify and extract latent components from raw time series data before applying anomaly detection or forecasting algorithms. These intermediate component extractions act as mediators that preserve measurement precision while enabling automated processing of complex temporal patterns.
2Device complexity
If models assume latent components are non-existent, then device complexity is reduced, but reliability deteriorates due to inaccurate forecasts
Solution Approach 1:
The patent implements self-service mechanisms where the decomposition algorithm automatically identifies and extracts latent components from the data without requiring manual configuration or assumptions about their presence. The system serves itself by adapting to the actual characteristics of each time series, maintaining model simplicity while improving forecast reliability through data-driven component identification.
Solution Approach 2:
The patent dynamically changes model parameters based on the detected latent components in the time series data. Rather than using fixed assumptions, the system adjusts its decomposition and analysis parameters to match the actual seasonal, level, and spike patterns present in each dataset, thereby maintaining simplicity while achieving high reliability.
3Measurement precision
If manual configuration is used to account for latent components, then measurement precision is improved, but loss of time increases due to reliance on analyst knowledge
Solution Approach 1:
The patent performs preliminary automatic decomposition of time series data into latent components before the main analysis task. This preliminary action automatically extracts seasonal, level, and spike patterns that would otherwise require manual analyst configuration, achieving high measurement precision while eliminating the time loss associated with manual knowledge-based setup.
Solution Approach 2:
The patent replaces the mechanical process of manual analyst configuration with an automated computational decomposition system. The algorithmic approach substitutes human expertise and manual time investment with automated statistical methods that achieve equal or superior precision in identifying latent components from the data.
Data Source
AI summary
Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.