Missing Data Segment Generation Using Breakpoint Detection and ARIMA
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Solution Overview
Problem
Existing methods for generating data in missing segments of time data sequences often fail to produce accurate results, especially when the missing segment includes multiple data points, as they do not conform to the actual conditions of the sequence.
Innovation Solution
A method and apparatus that determine whether a breakpoint exists in the missing segment, calculate candidate values using Autoregressive Integrated Moving Average (ARIMA) models based on data before and after the segment, and generate values by selectively using these candidates based on the presence of breakpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear interpolation or average value methods are used to generate data in missing segments, then the generation process is simple, but the accuracy of generated data deteriorates when the missing segment includes more than two data points
Solution Approach 1:
The patent divides the missing segment into multiple sub-segments based on detected breakpoints. Each sub-segment is then processed using appropriate interpolation methods, allowing different strategies for different portions of the missing data. This segmentation enables the system to handle complex missing segments while maintaining accuracy by treating each sub-segment according to its local characteristics.
Solution Approach 2:
The patent dynamically selects the data generation method based on the characteristics of each sub-segment. For sub-segments without breakpoints, linear interpolation or average values are used. For sub-segments containing breakpoints, more sophisticated methods like polynomial interpolation or curve fitting are applied to capture the local trend changes. This dynamic adaptation resolves the contradiction by adjusting method complexity to match actual data needs.
2Device complexity
If simple interpolation methods are used, then the computational complexity is low, but the conformity to actual conditions of the time data sequence deteriorates
Solution Approach 1:
The patent performs preliminary detection of breakpoints in the missing segment before generating data. By identifying these critical points in advance, the system can prepare appropriate generation methods for each sub-segment. This preliminary action allows the system to maintain low overall computational complexity while ensuring high conformity to actual conditions through targeted method selection.
Solution Approach 2:
The patent applies different data generation methods to different sub-segments based on their local characteristics. Sub-segments without breakpoints use simple methods, while sub-segments with breakpoints use more complex methods that better capture local trends. This local quality approach ensures computational efficiency is maintained where possible while achieving high reliability where needed.
Data Source
AI summary
A method and an apparatus for generating data in a missing segment of a target time data sequence are disclosed. The method includes: determining whether there is a breakpoint in the missing segment; determining candidate values of the data in the missing segment; and generating values of the data in the missing segment by selectively using the candidate values of the data in the missing segment, according to whether there is the breakpoint in the missing segment. With the method and the apparatus, the data in the missing segment of the target time data sequence can be generated more accurately.