Fitness Function Visualizations for Forecasting Algorithm Selection
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Solution Overview
Problem
Customer service centers face challenges in accurately forecasting future demand due to traditional approaches providing limited context, making it difficult to compare and select the most suitable forecasting algorithms, as they offer single forecast scores without showcasing distribution and patterns of deviations.
Innovation Solution
The system combines multiple forecasting algorithms with visualizations of their performance using fitness functions like RMSE and MAPE, generating distribution charts that highlight deviations within the forecast period, enabling users to make informed decisions and select the best algorithm based on specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional forecasting approaches provide only a single forecast score, then the forecasting process is simple and fast, but the user cannot understand the strengths and weaknesses of different algorithms or make informed decisions
Solution Approach 1:
The patent segments the single forecast score into multiple performance dimensions by implementing fitness functions that evaluate different aspects of forecast quality (e.g., accuracy, precision, recall, bias). This segmentation allows users to see the breakdown of performance metrics rather than a single aggregated score, thereby reducing information loss while maintaining manageable complexity through modular metric calculation.
Solution Approach 2:
The patent adds dimensional depth to forecast evaluation by introducing multiple performance dimensions (accuracy, precision, recall, bias, coverage) and visualizing them across different time horizons and demand levels. This dimensional expansion transforms a one-dimensional score into a multi-dimensional performance profile, enabling comprehensive algorithm comparison without overwhelming complexity through structured dimensional organization.
2Measurement precision
If multiple forecasting algorithms are evaluated with comprehensive metrics, then users can make informed decisions, but the evaluation process becomes complex and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing performance metrics for multiple algorithms across different time horizons and demand levels before user inquiry. The system pre-generates fitness function evaluations, accuracy metrics, precision metrics, recall metrics, bias measurements, and coverage statistics, allowing users to retrieve comprehensive comparisons instantly without experiencing the computational time burden during actual use.
Solution Approach 2:
The patent creates simplified copies of complex algorithm performances through visual representations. Instead of requiring users to analyze raw numerical data from multiple algorithms, the system generates visual copies (charts, graphs, and comparative displays) that replicate performance characteristics in an easily consumable format, maintaining measurement precision while dramatically reducing the time users need to spend on evaluation.
3Loss of information
If the system provides detailed visualizations of forecast deviations and performance distributions, then users gain deep insights into algorithm behavior, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies local quality by providing different levels of visualization detail tailored to specific user needs and contexts. Instead of uniformly complex visualizations across all scenarios, the system adjusts the granularity and depth of deviation distribution displays based on the selected time horizon, demand level, and user preferences, maintaining information completeness while managing complexity through localized adaptation of visualization intensity.
4Productivity
If the system automatically deploys optimized models based on user preferences, then productivity increases, but the automation level and system complexity increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select and deploy optimized forecasting algorithms based on user-expressed preferences and performance criteria. The system monitors user interactions with the visualization interface, learns from selection patterns, and autonomously deploys the most suitable algorithm without requiring manual intervention, thereby increasing productivity while managing automation complexity through preference-based decision logic.
Data Source
AI summary
Embodiments of the present disclosure provide systems and methods for generating a plurality of forecasts for a future time interval using a plurality of models and/or algorithms in order to assess the performance of each model. An example computer-implemented method can comprise generating a fitness function visualization corresponding with determined quantitative measures of forecast quality for each of the plurality of models and/or algorithms.