Transaction Data Gap Filling for ACD Forecasting
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Solution Overview
Problem
Automatic Call Distribution (ACD) centers face inefficiencies in staff scheduling due to tedious and time-consuming updates of forecasting models caused by gaps in transaction data, such as missing or invalid data, which affect forecasting accuracy.
Innovation Solution
A method and system that determine gaps in transaction data and use algorithms to fill them, either by identifying dominant patterns or employing the Moore-Penrose pseudo-inverse algorithm to select substitute data, thereby minimizing the impact on existing patterns and improving forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If forecasting models are updated with new transaction data, then forecasting accuracy is improved, but the process becomes tedious and time consuming
Solution Approach 1:
The system performs automatic gap detection and data imputation without requiring administrator intervention. The algorithm autonomously identifies missing transaction data, selects appropriate imputation methods, and completes the data, allowing the forecasting model to be updated automatically without manual effort.
Solution Approach 2:
The system performs preliminary data preparation by automatically detecting and filling gaps in transaction data before the forecasting model update process begins. This pre-processing step ensures that data is ready for modeling without requiring manual intervention during the update process.
2Measurement precision
If manual data updating is performed to fill gaps in transaction data, then forecasting accuracy is improved, but the process is tedious and time consuming
Solution Approach 1:
The system autonomously performs gap detection and data imputation without requiring administrator intervention. The algorithm automatically identifies missing transaction data, selects appropriate imputation methods based on data characteristics, and completes the data, making the process easy to operate.
Solution Approach 2:
The system replaces manual mechanical data entry and gap-filling operations with automated computational algorithms. The algorithm systematically processes transaction data, detects gaps, and imputes values using mathematical methods, substituting manual administrative work with automated computational processes.
3Productivity
If algorithms are used to automatically fill gaps in transaction data, then time and effort for data updates are reduced, but the complexity of the system increases
Solution Approach 1:
The system changes the parameter of data completeness by applying mathematical transformations and statistical methods to impute missing values. The algorithm adjusts data parameters through systematic computational processes, transforming incomplete transaction data into complete datasets suitable for forecasting.
Data Source
AI summary
A method and system to receive transaction data; determine a gap in the transaction data; and use an algorithm to generate data to fill in the gap is described. The algorithm is selected from a group including a first algorithm and a second algorithm. The first algorithm is to determine a dominant pattern in the transaction data; identify a region within the dominant pattern that corresponds to the gap in the transaction data; and adopt data associated with the corresponding region into the gap to minimize impact on the dominant pattern. The second algorithm includes a Moore-Penrose pseudo-inverse algorithm to choose the transaction data to fill in the gap based on a set of substitute data from among a group of substitute data sets and adopts the set of substitute data into the gap.