Cloud Analytics Rate Forecasting with Category-Level Savings Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack the ability to intelligently determine rate values for different categories of goods and services based on historical data, optimization levels, and user-defined aggressiveness, leading to inefficiencies in forecasting future expenditures and savings.
Innovation Solution
A computing system that utilizes historical transaction data to calculate average savings rates and standard deviations, combined with optimization levels and aggressiveness values, to determine intelligent rate values and forecast future savings, providing a graphical user interface for visualization and prioritization of categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical transaction data is analyzed to determine rate values, then forecasting accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the analysis by category, determining rate values separately for each category based on historical transactions. This allows complex analysis to be broken down into manageable category-specific computations, improving forecasting accuracy while keeping individual processing units simple.
Solution Approach 2:
The system uses historical transaction data to automatically determine rate values without requiring complex external inputs. The historical data itself serves as the foundation for calculating average savings rates and standard deviations, enabling the system to self-calibrate and improve forecasting accuracy autonomously.
2Reliability
If multiple categories are analyzed with different optimization levels, then decision-making quality is improved, but processing time increases
Solution Approach 1:
The system dynamically adjusts the level of analysis for each category based on optimization levels. Categories can be analyzed at different depths and granularities, allowing the system to allocate processing time proportionally to the importance and complexity of each category, thereby improving decision-making quality without uniformly increasing processing time.
Solution Approach 2:
Different optimization levels are applied to different categories based on their specific characteristics and importance. This local differentiation allows the system to spend more processing time on high-priority categories while using minimal processing for low-priority ones, optimizing the trade-off between decision-making quality and processing time.
3Reliability
If standard deviation is calculated from historical transactions, then rate value reliability is improved, but data processing complexity increases
Solution Approach 1:
The system calculates standard deviation as a statistical parameter to measure the variability of savings rates within each category. By transforming the raw transaction data into statistical parameters (mean, standard deviation), the system simplifies the representation of complex historical data while improving the reliability of determined rate values.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives, from a client device, a request for information associated with a category. In response to the request, the program further accesses a storage to retrieve a first value associated with the category. The program also determines a set of values associated with the category based on a plurality of transactions. The program further determines an optimization level value associated with the category. The program also determines a second value associated with the category based on the first value, the set of values, and the optimization level value. The program further provides, by an application operating on the device, a graphical user interface (GUI) to the client device, the GUI comprising the second value.