Cyclic Pattern Detection in Irregular Time Series Data
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Solution Overview
Problem
Existing data processing technologies face challenges in identifying cyclic patterns in data observations with irregular time spacing, leading to inaccurate prediction models and inefficient resource management in software applications and systems.
Innovation Solution
A computer-implemented method that analyzes data observations with irregular time intervals by defining a list of time gaps, using two reader operators to iteratively evaluate these gaps, and identifying cyclic patterns to generate a prediction model for future data observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to analyze data observations with irregular time spacing, then the analysis process is simple, but the accuracy of identifying cyclic patterns and generating prediction models deteriorates
Solution Approach 1:
The patent segments the analysis process into distinct components: (1) defining a list of time gaps between data observations, (2) using two separate reader operators to browse through time gaps, (3) iteratively evaluating time gaps to identify cycles, and (4) generating prediction models. This segmentation allows each component to be optimized independently, improving cyclic pattern identification accuracy while managing overall system complexity.
Solution Approach 2:
The patent transforms the one-dimensional time series data into a two-dimensional analysis space by creating a list of time gaps and using two reader operators that move at different speeds. This dimensional transformation enables the system to detect cyclic patterns in irregularly spaced data by comparing time gap relationships across different evaluation speeds, thereby improving measurement precision.
2Productivity
If manual intervention is used for data analysis and prediction model generation, then the system is easier to control, but productivity and efficiency deteriorate
Solution Approach 1:
The patent implements an automated system where the two reader operators independently browse through time gaps and automatically identify cyclic patterns without manual intervention. The system self-generates prediction models based on the detected cycles, significantly improving productivity. The automated nature maintains ease of operation through predefined algorithms that require minimal user configuration.
Solution Approach 2:
The system incorporates feedback mechanisms where the iteratively evaluated time gaps provide information about cyclic patterns, which then feeds into prediction model generation. This automated feedback loop enables continuous improvement of prediction accuracy while maintaining high productivity, as the system learns from its own analysis results without requiring manual reconfiguration.
3Measurement precision
If simple prediction models are used, then the system is easier to implement, but the accuracy of future data prediction deteriorates
Solution Approach 1:
The patent performs preliminary analysis by defining time gaps and using two reader operators to identify cyclic patterns before generating prediction models. This preliminary action extracts key characteristics from the data, allowing the subsequent prediction model to be both accurate and relatively simple. The cyclic pattern detection phase prepares the data in a form that enables efficient and accurate prediction without requiring overly complex models.
Solution Approach 2:
The patent replaces traditional mechanical prediction approaches with an algorithmic system that uses two reader operators moving at different speeds to detect cycles. This substitution enables the system to handle irregularly spaced data more effectively, generating accurate prediction models that capture complex temporal patterns without requiring equally complex model structures.
Data Source
AI summary
The present disclosure relates to computer-implemented methods, software, and systems for identifying cyclic patterns in data observations collected as time series with irregular time spacing between each other. Distribution of the time occurrences associated with the data observations is analyzed to identify a cyclic pattern. A list of time gaps between each of the data observations is defined. Time gaps are defined according to a common time measure. The time gaps of the list of time gaps are evaluated using two reader operators that separately browse through the list of time gaps. A cyclic pattern is identified in the list of time gaps based on the iteratively evaluating. The identifying comprises identifying (i) a length of cycle within the cycle patterns and (ii) an index element of a time gap of the list of time gaps.