Automated Frequency Recommendation for Time Series Data
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Solution Overview
Problem
Time series analysis at different frequencies is typically manual and time-consuming, requiring human effort to identify and compare insights across multiple frequencies, which can lead to inconsistent and less accurate results.
Innovation Solution
An automated framework that transforms input time series data into multiple frequency time series, calculates absolute percentage change and trend impact factors, and combines these to generate a frequency interest score, recommending optimal frequencies for analysis based on behavioral signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual time series analysis at different frequencies is performed, then human insight and judgment can be applied, but the process becomes time-consuming and less consistent
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated computational system. The system transforms input time series data into multiple frequency time series, calculates absolute percentage change and trend impact factors, and generates frequency interest scores automatically, eliminating the need for manual human analysis while maintaining or improving accuracy through consistent algorithmic application.
2Productivity
If automated frequency analysis is implemented, then time and resources are saved, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the complex analysis task into distinct computational modules: frequency transformation module, absolute percentage change calculation module, trend impact factor calculation module, and frequency interest score generation module. This segmentation allows the system to handle complexity through organized, independent processing steps that can be implemented and maintained separately.
Solution Approach 2:
The patent changes the parameter representation of time series data by transforming it across different frequencies and calculating derived parameters (absolute percentage change, trend impact factors). These parameter transformations enable automated frequency recommendation without requiring complex system architecture, as the complexity is managed through mathematical parameter relationships.
3Reliability
If manual frequency comparison is performed, then flexibility in analysis approach is maintained, but consistency and reliability of results decrease
Solution Approach 1:
The patent implements a self-service automated system that performs frequency analysis without requiring manual human intervention. The system automatically transforms data, calculates factors, generates scores, and provides frequency recommendations, ensuring consistent and reliable results through standardized algorithmic processes while simplifying the operational process for users.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for automatically recommending one or more frequencies for time series data. One example method includes receiving a request for an insight analysis for an input time series included in a dataset. For each of multiple frequencies to analyze, the input time series is transformed into a frequency time series. An absolute percentage change impact factor and an absolute trend impact factor are determined for each frequency time series. A frequency interest score is determined based on the determined absolute percentage change factors and the determined absolute trend impact factors, for each time frequency time series. The frequency interest score is provided for at least some of the frequency time series.