Automatic Intermittent Time Series Metadata Determination
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Solution Overview
Problem
Business intelligence systems face challenges in analyzing intermittent time series data, as existing methods struggle to automatically determine metadata describing time intervals, limiting the applicability of analytical and exploration techniques to only regular time series data.
Innovation Solution
A method and system for automatic interval metadata determination in intermittent time series data, which detects time variables, determines their regularity, calculates respective time intervals, and generates output parameters, enabling the analysis of intermittently regular data using regular time series techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing methods are used to analyze time series data, then regular time series data can be analyzed effectively, but intermittent time series data cannot be properly analyzed due to inability to automatically determine metadata
Solution Approach 1:
The system automatically determines time interval metadata by analyzing the time series data itself without requiring external input or manual configuration. The processing devices detect patterns in the data and self-determine the appropriate time intervals, allowing the system to serve itself in metadata generation.
Solution Approach 2:
The system changes the parameter representation of time series data by introducing metadata that describes time intervals. This transformation allows intermittent time series data to be converted into a format suitable for regular time series analysis techniques, effectively changing the data's temporal parameter structure.
2Productivity
If manual metadata determination is used, then accuracy can be maintained, but productivity and automation are reduced
Solution Approach 1:
The system uses feedback mechanisms where the detected time patterns are validated against the original data, and adjustments are made to refine the determined time intervals. This iterative feedback process ensures that automated determination achieves high accuracy comparable to manual methods.
Solution Approach 2:
The system performs preliminary analysis of the time series data to identify patterns and characteristics before finalizing metadata determination. This preliminary action includes examining data density, time distributions, and potential intervals, which prepares the system for accurate automated metadata generation.
Data Source
AI summary
Techniques are described for automatic interval metadata determination for intermittent time series data. In one example, a method for determining intermittent time series interval metadata includes detecting one or more time variables in a time series data set. The method further includes determining whether the one or more time variables are intermittently regular. The method further includes determining one or more respective time intervals for the one or more time variables. The method further includes determining the parameters of intermittency for the one or more time variables. The method further includes generating an output comprising information about the one or more time variables based on the one or more respective time intervals and the parameters of intermittency for the time variable.