Multiscale Predictive Alerting for Resource Forecasting
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Solution Overview
Problem
Existing time-series forecasting methods, such as linear regression and Holt-Winters models, fail to accurately account for both long-term and short-term patterns, leading to false positives and negatives in predictive alerts, which can result in inefficient resource allocation and increased costs due to over or underutilization of hardware and software resources.
Innovation Solution
A system and method that generate predictive alerts by combining long-term seasonal models with short-term trend-based models, using Holt-Winters additive or multiplicative models for long-term patterns and trend-based analytics for short-term patterns, to project future resource usage and trigger alerts when thresholds are met, thereby providing early warnings for resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Holt-Winters model is used for long-term seasonal patterns, then accuracy in capturing seasonal trends is improved, but false positives increase due to inability to account for short-term patterns
Solution Approach 1:
The forecasting system is segmented into two independent model components: a Holt-Winters model for long-term seasonal patterns and a trend-based model for short-term patterns. Each model operates independently on its designated time scale, with results combined to produce the final forecast. This segmentation allows each model to excel at its specific function without interfering with the other, resolving the contradiction between capturing seasonal trends and avoiding false positives from short-term fluctuations.
Solution Approach 2:
The system merges the forecasts from the Holt-Winters seasonal model and the trend-based short-term model into a unified forecasting result. By combining the strengths of both models—seasonal pattern recognition from Holt-Winters and short-term trend capture from the trend-based model—the system achieves both high measurement precision and reliability, eliminating false positives while maintaining accurate seasonal forecasting.
2Reliability
If resources are deployed to handle peak demand, then service level agreement compliance is improved, but costs increase due to underutilization during low-demand periods
Solution Approach 1:
The system performs preliminary forecasting by combining seasonal and short-term trend patterns to predict future resource usage before peak demand occurs. This advance prediction allows organizations to proactively allocate resources in advance of actual demand spikes, ensuring service level agreement compliance during peak periods while avoiding the need to maintain excessive resources during low-demand periods, thus improving overall resource utilization efficiency.
3Loss of energy
If resources are reduced to minimize costs, then resource utilization efficiency is improved, but false negatives increase leading to insufficient resources during peak times
Solution Approach 1:
By performing preliminary combined forecasting that accounts for both seasonal patterns and short-term trends, the system identifies upcoming resource requirements in advance. This allows organizations to maintain optimized resource levels during low-demand periods while receiving early warnings before peak demand occurs, preventing false negatives and ensuring sufficient resources are available when needed without permanently over-provisioning resources.
Data Source
AI summary
Techniques are described for generating predictive alerts. In one or more embodiments, a seasonal model is generated, the seasonal model representing one or more seasonal patterns within a first set of time-series data, the first set of time-series data comprising data points from a first range of time. A trend-based model is also generated to represent trending patterns within a second set of time-series data comprising data points from a second range of time that is different than the first range of time. A set of forecasted values is generated based on the seasonal model and the trend-based model. Responsive to determining that a set of alerting thresholds has been satisfied based on the set of forecasted values, an alert is generated.