IT Resource Forecasting via Multi-Model Time Series Analysis
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Solution Overview
Problem
Current information technology resource forecasting methodologies suffer from low accuracy due to the assumption that one statistical model can fit all resource trends, leading to uninformed decisions and a reliance on manual, time-consuming methods that are prone to errors and single points of failure.
Innovation Solution
Implementing a system that uses multiple time series models to create forecasts for information technology resources, selecting the most accurate model based on historical and real-time data, and providing real-time visibility to resource status to detect and react to unexpected scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and processing methods are used across different teams and systems, then flexibility and adaptability are maintained, but forecasting accuracy is low and the process is time-consuming
Solution Approach 1:
The patent replaces manual mechanical data collection and processing with automated computational systems. Machine learning models and algorithms automatically ingest, clean, and analyze data from multiple sources, eliminating manual effort while improving forecasting accuracy through sophisticated pattern recognition capabilities that exceed human analytical capacity.
Solution Approach 2:
The forecasting system implements self-service capabilities by automatically collecting data from integrated sources, performing quality validation, selecting appropriate models, and generating forecasts without human intervention. The system self-adjusts parameters and retrain models based on new data, reducing both time consumption and human error while maintaining continuous operation.
2Measurement precision
If a single statistical model is used to fit all resource trends, then device complexity is reduced, but forecasting accuracy deteriorates due to inability to capture diverse patterns
Solution Approach 1:
The patent segments the forecasting system into multiple specialized statistical models, each designed to capture specific resource trends and patterns. Different models handle different data characteristics (e.g., linear growth, seasonal patterns, exponential trends), allowing the system to achieve high accuracy across diverse resource types while managing complexity through modular architecture and automated model selection.
Data Source
AI summary
Information technology resource forecasting based on time series analysis is described. A system creates multiple forecasts for an information technology resource by applying corresponding multiple time series models to first data associated with the information technology resource. The system selects a model of the multiple time series models by comparing the multiple forecasts for the information technology resource to second data associated with the information technology resource. The system outputs a forecast that is created by applying the selected model to third data associated with the information technology resource.