Time Series Anomaly Forecasting With Adaptive Sensitivity Boundaries
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Solution Overview
Problem
Conventional time series forecasting techniques struggle to accurately predict recurring anomalies in dynamic environments, particularly in network traffic analysis, due to limitations in capturing intricate patterns and seasonality trends, leading to inefficient resource management and suboptimal decision-making.
Innovation Solution
A novel methodology that integrates trend forecasting with automated anomaly detection using sensitivity boundaries, combining historical and forecasted data to identify deviations from predefined interval widths, enabling proactive alerts and resource scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional time series forecasting techniques are used, then the forecasting process is simple, but the accuracy of predicting recurring anomalies is insufficient
Solution Approach 1:
The patent combines multiple forecasting techniques (trend forecasting, seasonality analysis, confidence intervals) into a unified framework. This integration allows the system to capture intricate patterns and recurring anomalies more accurately while managing complexity through a cohesive architecture rather than separate disparate components.
Solution Approach 2:
The system dynamically adjusts sensitivity boundaries based on forecasted confidence intervals. The anomaly detection threshold is not fixed but adapts to the uncertainty of predictions, allowing the system to maintain high accuracy across varying conditions while managing complexity through adaptive rather than static parameters.
2Measurement precision
If sensitivity boundaries with predefined interval widths are used, then anomaly detection precision is improved, but the complexity of determining normal operating ranges increases
Solution Approach 1:
The system pre-determines sensitivity boundaries and normal operating ranges based on historical data and forecasted confidence intervals before actual anomaly detection occurs. This preliminary setup phase separates the complex determination process from the real-time detection process, improving detection precision while managing complexity through advance preparation.
Solution Approach 2:
The system changes the parameter of sensitivity boundary width based on the confidence interval of forecasts. By dynamically adjusting this parameter according to prediction uncertainty, the system achieves high detection precision without requiring complex manual configuration, as the parameter adapts automatically based on data characteristics.
3Productivity
If manual anomaly detection methods are used, then the system complexity is low, but the productivity and timeliness of anomaly identification decrease
Solution Approach 1:
The system performs automated anomaly detection using forecasted data and pre-determined sensitivity boundaries without requiring manual intervention. The forecasting model itself serves the dual purpose of prediction and anomaly identification, improving productivity by automating the process while managing complexity through self-service rather than external manual analysis.
Solution Approach 2:
The system uses feedback from forecasted confidence intervals to automatically adjust sensitivity boundaries and trigger alerts. This closed-loop feedback mechanism enables high-speed automated anomaly identification by continuously comparing actual data against forecasted ranges and reacting immediately when anomalies are detected, eliminating manual review delays.
4Measurement precision
If comprehensive trend forecasting and seasonality analysis are integrated, then forecasting accuracy is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent segments the forecasting process into distinct components: trend forecasting, seasonality analysis, and confidence interval calculation. Each component processes specific aspects of the data independently, improving overall forecasting accuracy through comprehensive analysis while managing computational resources by dividing the workload into manageable segments rather than monolithic processing.
Data Source
AI summary
Systems and methods for forecasting recurring anomalies in time series data include determining forecast data from historical data of time series data where the forecast data is for a desired future time span; combining the forecast data with the historical data to form a combined time series; determining a normal operating range using the combined time series; detecting anomalies in the forecast data based on the normal operating range; and providing an output including the detected anomalies in the forecast data.