Time-Series Input Correction for Defect Detection and Prediction
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Solution Overview
Problem
Computer technologies face challenges in accurately analyzing user-input time-series data due to inconsistencies and errors, which affect the validity and reliability of generated insights and predictions, particularly in fields like fitness tracking where data is multidimensional and sensitive to missing or misreported information.
Innovation Solution
A computer-implemented method and system that uses machine learning algorithms to detect defects in user-input time-series data by comparing predicted and measured values, identifies the root cause of defects, and corrects the data through computational methods, such as Kalman filtering, to provide accurate and personalized insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user-input time-series data is used directly for analysis, then data collection is simple and quick, but the accuracy and reliability of generated insights and predictions deteriorate due to inconsistencies and errors
Solution Approach 1:
The system performs preliminary defect detection and root cause analysis on user-input data before the data is used for generating insights and predictions. By proactively identifying and flagging potential errors, missing values, and inconsistencies in advance, the system prevents inaccurate data from compromising the analytical results, thus maintaining both efficient data collection and high data accuracy.
Solution Approach 2:
The system implements a feedback mechanism where detected defects and their root causes are communicated back to users through prompts. This feedback loop enables users to understand and correct data issues, improving data quality while maintaining the simplicity of data collection. The system continuously learns from user corrections to enhance its defect detection capabilities.
2Measurement precision
If machine learning algorithms are used to detect defects and identify root causes, then data accuracy improves, but system complexity increases
Solution Approach 1:
The system employs machine learning algorithms that automatically detect defects, analyze patterns, and identify root causes without requiring manual intervention or complex configuration. The algorithms self-adjust and learn from data patterns, enabling accurate defect detection while keeping the system relatively simple to operate. Users simply input data and receive automated analysis results.
3Reliability
If the system prompts users to review and correct defects, then data quality improves, but time consumption increases
Solution Approach 1:
The system performs preliminary defect detection and prepares suggested corrections before presenting prompts to users. By pre-analyzing data issues and formulating potential solutions in advance, the system reduces the time users need to spend on corrections. Users receive well-prepared, targeted prompts with specific issues and suggested fixes rather than having to manually review entire datasets.
Solution Approach 2:
The system provides targeted feedback to users about specific defects and their likely root causes, enabling quick and informed corrections. The feedback mechanism is designed to be efficient, presenting only the most relevant issues that need user attention, thus improving data quality while minimizing time investment from users.
Data Source
AI summary
Technical solutions are described that address correcting input time-series data provided for analysis and predictions. An example computer-implemented method includes receiving, by a processor, a time-series data input by a user. The computer-implemented method also includes computing, by the processor, a first plurality of predicted values based on the time-series data input by the user; computing, by the processor, a second plurality of predicted values by. The computer-implemented method also includes determining estimated time-series data based on the time-series data input by the user. The computer-implemented method also includes computing the second plurality of predicted values based on the estimated time-series data. The computer-implemented method also includes determining, by the processor, a defect in the time-series data input by the user based on a distribution of a plurality of differences between respective values from the first plurality of predicted values and the second plurality of predicted values.