Resource Demand Forecasting With Segmented Time-Series Modeling
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Solution Overview
Problem
Conventional resource forecasting techniques fail to accurately forecast resources associated with irregularities in time and demand quantity, leading to errors, inaccuracies, and resource wastage.
Innovation Solution
A method involving segmentation of resource demand time series data based on statistical analysis, determining probability distributions for each segment, and generating forecasts using machine learning techniques to align with future time periods, followed by automated actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional resource forecasting techniques are used, then the forecasting process is simple, but accuracy and reliability are poor leading to errors and resource wastage
Solution Approach 1:
The patent segments the resource demand time series data into multiple segments based on demand level thresholds. This segmentation allows the system to apply different probability distributions to different demand patterns (e.g., normal distribution for regular demand, log-normal for irregular demand), thereby improving forecasting accuracy for complex and irregular patterns while maintaining a systematic approach.
Solution Approach 2:
The patent changes the parameters of probability distributions based on the segmented demand patterns. By determining appropriate probability distributions (normal, log-normal, exponential, etc.) for each segment and using machine learning to align forecasts to future time periods, the system adapts to varying demand characteristics, improving reliability without requiring a completely new forecasting methodology.
2Measurement precision
If conventional forecasting methods are used, then the system is simple to implement, but measurement precision and forecasting detail are insufficient for irregular patterns
Solution Approach 1:
The system segments the time series data into multiple demand-level segments, allowing precise measurement and modeling of different demand patterns. This segmentation enables the system to capture irregular patterns by applying appropriate probability distributions to each segment, thereby improving measurement precision without overwhelming complexity.
Solution Approach 2:
The patent uses probability distributions as intermediary models between the raw demand data and the final forecasts. These distributions serve as mediators that transform segmented demand data into probabilistic forecasts, which are then aligned to future time periods using machine learning techniques, achieving precise measurements while managing processing complexity.
3Productivity
If automated actions are implemented based on forecasts, then productivity and resource allocation improve, but the system complexity and automation extent increase
Solution Approach 1:
The patent implements automated actions based on the generated forecasts, creating a feedback loop where forecast results are used to drive resource allocation decisions. This feedback mechanism improves productivity by ensuring resources are allocated efficiently based on accurate forecasts, while the automation extent is managed through systematic decision rules rather than complex adaptive systems.
Solution Approach 2:
The system performs self-service automation by automatically generating forecasts and executing resource allocation decisions based on predefined criteria. The machine learning alignment process and automated action execution reduce the need for manual intervention, thereby improving productivity while keeping the automation system complexity manageable through standardized processes.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automated resource forecasting using statistical analysis and machine learning techniques are provided herein. An example computer-implemented method includes segmenting resource demand time series data, for at least one resource, into segments based on at least one resource demand level threshold; determining at least one probability distribution that fits at least a plurality of the segments using one or more statistical analyses; generating forecasts for two or more of the segments for at least one future time period based on the at least one probability distribution; generating a resource demand forecast for the at least one resource by aligning the forecasts for the two or more of the segments to at least one time index associated with the at least one future time period using one or more machine learning techniques; and performing one or more automated actions based on the resource demand forecast.