Empirical Bayesian Deconvolution Forecasting Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing forecasting techniques often rely on peer group-based methods that may not accurately reflect the homogeneity of items, leading to less accurate predictions due to arbitrary group definitions and insufficient data, especially in fast-moving environments with large and rapidly changing item sets.
Innovation Solution
An empirical Bayesian methodology employing a deconvolution algorithm that generates forecasts by combining category-level and item-level distributions, with a category-versus-item adjustment based on the number of records and dispersion metrics, allowing for a smooth transition from category to item-level information as more data becomes available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If peer group based prediction techniques are used, then forecasts can be generated for items with limited individual data, but the accuracy of forecasts deteriorates due to arbitrary group definitions and insufficient reflection of actual homogeneity
Solution Approach 1:
The patent changes the parameter of group definition from arbitrary categorical grouping to statistically-derived clusters based on forecast error characteristics. By using parameters such as forecast error variance and correlation with category forecasts, items are dynamically grouped into peer groups that reflect actual homogeneity rather than predefined categories, thereby improving forecast accuracy without excessive complexity
Solution Approach 2:
The patent replaces the mechanical/manual process of defining peer groups based on item attributes with an automated statistical methodology. The system automatically computes forecast errors, correlates them across items, and assigns peer group memberships based on statistical similarity, eliminating the need for manual categorization and arbitrary thresholds
2Reliability
If peer group based prediction techniques are used, then forecasts can be generated when individual item data is insufficient, but the minimum size of acceptable peer group must be defined arbitrarily
Solution Approach 1:
The patent makes the peer group size dynamic rather than fixed. The minimum peer group size adapts automatically based on the statistical properties of the data, specifically the forecast error characteristics and correlation strength between items. This allows the system to flexibly adjust peer group composition and size to match the actual data availability and homogeneity, eliminating arbitrary size constraints
3Reliability
If more peer group items are included to improve data sufficiency, then forecast reliability improves, but the homogeneity among group members deteriorates due to arbitrary grouping
Solution Approach 1:
The patent incorporates feedback from forecast error analysis to continuously refine peer group definitions. By monitoring the forecast errors of items in peer groups and adjusting group memberships based on error correlation, the system ensures that peer groups maintain both sufficient size for statistical reliability and adequate homogeneity for accurate error modeling. This feedback loop prevents degradation of homogeneity as group size increases
Data Source
AI summary
A data set comprising records of state change events of items of an item collection, as well as records of asynchronous operations associated with the items, is obtained. The numbers of records in the data set may differ from one item to another. Using the data set, a Bayesian forecasting model employing a deconvolution algorithm is trained. The model generates estimates of metrics of a type of asynchronous operation using a combination of a category-level distribution of the asynchronous operation, an item-level distribution, and a category-versus item adjustment. A trained version of the model is stored.