Time-Series Data Analysis Apparatus for Composite Factor Extraction
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Solution Overview
Problem
Existing methods for analyzing multivariate time-series data, such as covariance analysis and hidden Markov models, face challenges in identifying composite factors without hypothetical bases, especially in large or complex datasets, and struggle to extract transitional patterns effectively.
Innovation Solution
A time-series data analyzing apparatus that divides data into pattern generation and inspection sets, generates transitional patterns with high transition occurrence probabilities, computes cause-and-effect strengths, and displays composite factor patterns with significant cause-and-effect relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If covariance analysis or hidden Markov model is used to analyze multivariate time-series data, then the analysis can take into account composite factors, but the method requires strict prerequisites such as normality of distribution and parallelism of regression line, or requires the analyzer to consider dependency relationship carefully before analysis, making it difficult to extract composite transitional patterns from large or complicated data without a hypothetical basis
Solution Approach 1:
The patent segments the analysis process into distinct modules: a dividing device that separates time-series data into pattern generation data and pattern inspection data, a first generating device that creates transitional patterns from the pattern generation data, a second generating device that integrates frequently appearing patterns, and a second computing device that computes cause-and-effect strength. This segmentation allows each module to handle specific tasks with simplified logic, avoiding the need for complex overall analysis while maintaining accuracy in identifying composite factors.
2Loss of information
If traditional analysis methods are used, then the analysis framework is established, but it is considered difficult to pick out only the related composite factor from large amounts of data or complicated data without a hypothetical basis
Solution Approach 1:
The patent performs preliminary actions by first dividing the time-series data into pattern generation data and pattern inspection data before analysis. The first generating device then creates transitional patterns based on this pre-processed data, identifying frequently appearing patterns in advance. This preliminary organization of data and patterns enables efficient extraction of composite factors without requiring complex hypothetical frameworks, as the relevant patterns are already prepared and organized for analysis.
Data Source
AI summary
A time-series data analyzing apparatus which extracts a composite factor time-series pattern from time-series data. The apparatus includes a dividing device which divides the time-series data into pattern generation time-series data and pattern inspection time-series data which do not include pattern generation time-series data. A first generating device generates a transitional pattern including a support time data indicating a transition of support time and having a transition occurrence probability higher than a minimum occurrence probability in the pattern generation time-series data. A second generating device generates frequently appearing integrated transitional patterns. A second computing device computes cause-and-effect strength of each of the frequently appearing integrated transitional patterns using the pattern inspection time-series data. A display device displays the composite factor time-series pattern having the cause-and-effect strength higher than the minimum cause-and-effect strength given preliminarily.