Demand Prediction Using Regression Trend Extrapolation
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Solution Overview
Problem
Conventional data mining techniques struggle to accurately predict future demand by analyzing frequent patterns in databases, as they either prioritize recent data for relevance but lose statistical significance or use longer time frames for confidence but decrease accuracy due to older data inclusion.
Innovation Solution
The system employs numerical calculation techniques like least-squares approximation and regression analysis over discrete time slices to derive trend information, focusing on recent data for more accurate frequency predictions and projecting future behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shorter and more recent time periods are used for determining pattern frequency, then accuracy is improved, but statistical significance is reduced
Solution Approach 1:
The patent segments the time period into multiple discrete time slices (e.g., weekly, monthly intervals) rather than using a single continuous period. This allows the system to analyze patterns in recent data while maintaining statistical significance through the structured breakdown of time-based segments, resolving the contradiction between using short recent periods for accuracy and long periods for statistical significance.
Solution Approach 2:
The patent implements dynamic weighting of time slices where more recent time slices are assigned higher weights in the frequency calculation. This dynamic approach allows recent patterns to have greater influence on predictions (improving accuracy) while still incorporating historical data with reduced weights (maintaining statistical significance), thus resolving the contradiction between accuracy and reliability.
2Reliability
If longer time periods are used for determining pattern frequency, then statistical confidence is improved, but accuracy decreases due to inclusion of older data
Solution Approach 1:
By segmenting the longer time period into discrete weighted time slices, the system can incorporate historical data for statistical confidence while structuring it so that older segments contribute less to the final frequency calculation, thereby maintaining accuracy despite using longer time periods.
Solution Approach 2:
The dynamic weighting scheme applied to different time slices allows the system to use longer time periods for analysis (improving statistical confidence) while automatically reducing the influence of older data through decreasing weights, thus preventing accuracy degradation from including outdated patterns.
3Adaptability or versatility
If candidate set generation is performed to identify frequent patterns, then comprehensive pattern discovery is improved, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary filtering and preprocessing of the transaction database before candidate set generation, organizing data into time-sliced structures and pre-identifying potentially frequent patterns. This preliminary action reduces the search space for subsequent candidate generation, achieving comprehensive pattern discovery with reduced computational resources.
Solution Approach 2:
By segmenting the database into time slices and processing each slice separately, the patent reduces the computational complexity of candidate set generation. The segmentation allows the system to discover patterns comprehensively across different time periods while consuming fewer resources compared to analyzing the entire database as a single large set.
Data Source
AI summary
A data processing system processes transaction database information to predict future demand using data regression techniques to extract trend line information from historical pattern frequency values. By extrapolating the trend line, a predicted pattern frequency value may be calculated. By applying regression techniques (such as least-squares approximation), the trend line information may be extracted and projected to predict the future pattern frequency which may be applied to calculate the expected value of a recommendation rule.