Anomaly Prediction Using Probabilistic Distribution Parameters
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Solution Overview
Problem
Existing computerized systems face challenges in accurately predicting future anomalies, such as those in network environments, due to the difficulty in analyzing time-series data to determine the types, volume, scope, and timing of future anomalous events.
Innovation Solution
The use of a probabilistic model, such as a Gaussian mixture model, to analyze historical time-series data and determine distributions for normal and anomalous events, allowing for the calculation of distribution parameters like mean, standard deviation, and density, which are then used to build a classifier to predict future anomalies with confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical time-series data is analyzed using traditional methods, then data processing is simple, but prediction accuracy of future anomalies is insufficient
Solution Approach 1:
The patent transforms the prediction problem by changing the parameter representation from raw time-series values to probability distributions characterized by parameters (mean, standard deviation, shape). This allows the model to capture anomaly patterns through distributional parameters rather than direct value prediction, improving accuracy while managing complexity through parameterized representations.
Solution Approach 2:
The patent introduces probability distributions as an intermediary layer between historical data and future predictions. Instead of directly predicting anomaly values, the system models the distribution of normal and anomalous events, using these distributions as mediators to infer future anomaly characteristics. This intermediary approach resolves the contradiction by providing a structured framework that improves prediction accuracy without requiring overly complex direct prediction models.
2Measurement precision
If probabilistic models are used to analyze historical data, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent reduces computational complexity by changing from modeling entire time-series distributions to modeling only key parameters (mean, standard deviation, shape parameters). This parameterization approach allows the system to capture essential anomaly characteristics while significantly reducing the computational burden compared to full distributional modeling or complex machine learning approaches.
Solution Approach 2:
The patent extracts only the essential characteristics needed for anomaly detection from the full historical data distribution. By identifying and modeling only the critical parameters that distinguish normal from anomalous events, the system achieves accurate anomaly detection without processing the complete complexity of the underlying data distributions, thereby reducing computational resource requirements.
3Reliability
If distribution parameters are calculated for classification, then anomaly identification improves, but data processing time increases
Solution Approach 1:
The patent improves processing efficiency by changing from analyzing full distribution functions to calculating only essential parameters (mean, standard deviation, shape). This parameter extraction approach maintains classification reliability by focusing on the most discriminative features while significantly reducing the computational time required for data processing and anomaly identification.
Solution Approach 2:
The patent applies partial action by calculating only the specific distribution parameters necessary for anomaly classification rather than computing complete distribution functions or performing exhaustive analyses. By selectively computing only the mean, standard deviation, and shape parameters needed for discrimination, the system achieves reliable anomaly identification with reduced processing time compared to comprehensive distributional analysis.
Data Source
AI summary
Historical time-series data can be analyzed using a probabilistic model to determine one or more distributions, including at least a normal distribution and an anomaly distribution. These distributions can be analyzed to obtain values for distribution parameters, such as mean, standard deviation, and density, as well as other statistical parameters, for use in building a forecasting model. This model can analyze the time-series data to predict or forecast actionable anomalies at one or more future points or periods in time, such as may exceed a determined anomaly threshold with at least a minimum amount of confidence. A determination can be made as to one or more actions to take in anticipation of the anomalous event, or volume of events, such as to attempt to prevent the occurrence or to be better positioned to handle the occurrence. Such forecasting or prediction can utilize both modeling and feature engineering.