Irregular Time Interval Forecasting for Seasonal Sales Data
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Solution Overview
Problem
Existing data forecasting methods struggle with irregular time periods, leading to inaccurate predictions due to assumptions of uniform and evenly spaced intervals, which are not suitable for seasonal data with inactive periods, resulting in large numbers of zeros and difficulty in identifying proper time series models.
Innovation Solution
A computer-implemented system and method for generating future sales forecasts by converting input sales data into irregular time periods, assigning sums of sales to single converted time periods, and generating a predictive data model using regression analysis, allowing for custom intervals that accommodate seasonal and inactive periods, such as those around holidays or varying business days.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If evenly spaced time intervals are used for data forecasting, then the data can be modeled as continuous and smooth, but the model becomes inaccurate for seasonal data with inactive periods, resulting in large numbers of zeros and poor prediction performance
Solution Approach 1:
The patent applies dynamics by making the time interval structure adaptive rather than fixed. The system dynamically adjusts time intervals based on seasonal patterns and business rules, allowing the forecasting model to accommodate varying active and inactive periods. This is achieved through configurable time interval definitions that can be modified to match different seasonal cycles, thereby maintaining forecasting accuracy across diverse seasonal scenarios.
Solution Approach 2:
The patent changes the parameter of time interval length from uniform to variable. By allowing time intervals to vary in length according to seasonal patterns and business rules, the model can accurately represent periods of high and low activity. This parameter change enables the forecasting system to handle irregular seasonal data without being constrained by fixed evenly-spaced intervals, directly improving reliability for seasonal forecasting.
2Ease of manufacture
If uniform time periods are used to aggregate sales data, then the data structure is simple and easy to model, but the model cannot accommodate inactive periods or seasonal variations, leading to poor forecast quality
Solution Approach 1:
The patent applies segmentation by dividing the time series data into distinct segments corresponding to active and inactive periods. This segmentation allows the model to treat different portions of the data differently, applying appropriate aggregation rules to each segment. By segmenting the data according to seasonal patterns and business rules, the system maintains ease of modeling while significantly improving forecast quality for seasonal items.
Solution Approach 2:
The patent makes the data aggregation structure dynamic by allowing time period definitions to change based on seasonal patterns. Rather than using fixed uniform periods, the system dynamically adjusts aggregation intervals to match active and inactive periods, making the modeling process adaptable to seasonal variations while maintaining structural simplicity through rule-based definitions.
3Ease of operation
If standard time intervals are used for forecasting, then the modeling process is straightforward, but the system cannot handle non-standard fiscal periods or holiday-related seasonal variations
Solution Approach 1:
The patent applies universality by creating a forecasting system that can handle multiple types of time intervals through a single unified framework. The configurable time interval definitions allow the same forecasting model to work with standard intervals, non-standard fiscal periods, and holiday-related variations. This multi-functional approach maintains ease of operation while significantly expanding adaptability to diverse business scenarios.
Solution Approach 2:
The patent enables parameter changes by allowing time interval definitions to be customized according to specific business needs. The system can adjust interval lengths, start dates, and patterns to match non-standard fiscal periods or holiday cycles, all within the same forecasting framework. This parameter flexibility maintains operational simplicity while achieving high versatility for custom scenarios.
4Reliability
If irregular time periods are used to accurately represent seasonal data, then forecasting accuracy improves, but the data structure becomes more complex and harder to model
Solution Approach 1:
The patent manages complexity by making the irregular time period structure dynamic and rule-based rather than static and ad-hoc. The system uses configurable business rules to generate irregular intervals, which simplifies the modeling process compared to handling arbitrary irregular periods. This dynamic approach maintains high forecasting accuracy while reducing structural complexity through systematic rule application.
Solution Approach 2:
The patent handles irregular time periods by changing the parameter structure from fixed to variable in a controlled manner. By defining variable intervals through configurable parameters and business rules, the system achieves the necessary complexity for accurate seasonal forecasting without creating unmanageable structural complexity. The rule-based parameter changes keep the data structure organized and modelable.
Data Source
AI summary
Systems and methods are provided for segmenting time-series data stored in data segments containing one or more data records. A combined segment error measure is determined based on a proposed combination of two candidate segments. An error cost to merge the two candidate segments is determined based on a difference between the combined segment error measure and a segment error measure of one of the segments. The two candidate segments are combined when the error cost to merge meets a merge threshold to generate a combined segment.