Factor Analysis Device for Noisy Time Series Preprocessing
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Solution Overview
Problem
Existing factor analysis methods struggle with analyzing data from production processes that include noisy observation values, as they require subjective preprocessing decisions to improve analysis accuracy, making it difficult for those without prior knowledge to determine appropriate preprocessing techniques.
Innovation Solution
A factor analysis device and method that includes feature extraction, conversion, and influence degree computation units to objectively identify appropriate preprocessing for explanatory time series and determine their influence on response time series changes, using feature and explanatory-time-series influence-degree computation units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If preprocessing is applied to noisy observation data to improve analysis accuracy, then measurement precision is improved, but device complexity increases due to subjective preprocessing decisions
Solution Approach 1:
The system automatically determines and applies appropriate preprocessing methods for each explanatory variable based on its own characteristics (noise level, trend, seasonality) without requiring external subjective judgment. The preprocessing selection and parameter determination are performed autonomously by the analysis device itself.
Solution Approach 2:
The system changes the parameters of the explanatory variables through automated preprocessing operations (smoothing, differencing, normalization) based on detected data characteristics, transforming noisy raw data into analysis-ready formatted data with optimized parameters for each variable type.
2Ease of operation
If automated preprocessing is implemented to reduce subjectivity, then ease of operation is improved, but measurement precision may deteriorate due to loss of expert judgment
Solution Approach 1:
The system incorporates feedback mechanisms that evaluate the effectiveness of applied preprocessing and adjust preprocessing parameters accordingly. The analysis results feed back into the preprocessing stage, allowing iterative optimization that maintains high precision while keeping the system automated and easy to operate.
Solution Approach 2:
The patent replaces the mechanical system of expert human judgment with an automated computational system that uses algorithms to detect data characteristics and select preprocessing methods, achieving both ease of operation and maintained precision through systematic automated decision-making.
3Measurement precision
If multiple multivariate analysis methods are used to compute influence degrees, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple multivariate analysis methods (regression analysis, correlation analysis, principal component analysis) into a unified analysis framework that computes influence degrees using all methods simultaneously, combining their strengths to achieve more accurate and robust influence degree measurements while managing complexity through integration.
Data Source
AI summary
This factor analysis device is provided with a feature extraction unit (1021) that extracts feature quantities from an explanatory time series, a feature conversion unit (1022) that converts said feature quantities to a feature time series, a feature-time-series influence-degree computation unit (1031) that uses said feature time series and a response time series to compute an influence degree indicating the degree to which the feature time series influences the change over time represented by the response time series, and an explanatory-time-series influence-degree computation unit (1032) that uses said influence degree to compute an influence degree indicating the degree to which the explanatory time series influences the change over time represented by the response time series.