Geospatial Temporal Event Detection via Adaptive Volatility Thresholds
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Solution Overview
Problem
Conventional technologies lack effective methods for analyzing geospatial temporal data, relying on data-independent rules of thumb that impose arbitrary constraints on volatility analysis, limiting the ability to identify and predict events of interest in high-volume temporal datasets.
Innovation Solution
The system automatically detects relative volatility and amplitude in geospatial temporal data by computing dataset-specific volatility periods and thresholds using spectral density analysis, extracting features, and applying moving averages and standard deviations to identify events of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data-independent rules of thumb are used for volatility analysis, then the analysis process is simple and easy to implement, but the analysis lacks context-sensitivity and imposes arbitrary constraints on the data
Solution Approach 1:
The patent changes the parameters from fixed rule-based values to dataset-specific parameters derived through spectral density analysis. The system automatically determines the volatility period by analyzing the spectral density of the specific dataset, then uses this to calculate dataset-specific volatility thresholds. This transforms the analysis from using arbitrary fixed parameters to using parameters adapted to each dataset's characteristics, resolving the contradiction between ease of operation and context-sensitivity.
2Ease of manufacture
If conventional volatility measures with fixed thresholds are used, then the implementation is straightforward, but the ability to identify and predict events of interest in high-volume temporal datasets is limited
Solution Approach 1:
The patent applies preliminary action by performing spectral density analysis and automatically determining the volatility period before calculating volatility thresholds. This preliminary characterization of the dataset's frequency content allows the system to set data-adaptive thresholds that are optimized for detecting events in that specific dataset, thereby improving event detection accuracy while maintaining straightforward implementation through automation.
Solution Approach 2:
The system performs self-service by automatically analyzing the spectral density of the input dataset and determining its own volatility period and thresholds without requiring external configuration or expert knowledge. The dataset essentially serves itself by providing the frequency information needed to establish its own analysis parameters, improving both implementation ease and detection precision.
3Device complexity
If arbitrary constraints are imposed on volatility analysis parameters, then the analysis framework is simplified, but the detection of outlier conditions and events of interest becomes less effective
Solution Approach 1:
The patent introduces dynamics by making the volatility period and thresholds adaptive to each dataset rather than fixed. The system dynamically determines the appropriate volatility period through spectral density analysis of the specific dataset being analyzed, allowing the analysis framework to adapt its parameters to match the characteristics of the data. This dynamic adaptation improves outlier detection effectiveness without significantly increasing framework complexity, as the adaptation is automated.
Data Source
AI summary
Various technologies pertaining to automatic relative volatility and relative amplitude detection are described herein. A spectral density of a geospatial temporal dataset is computed, and one or more frequencies of the dataset are identified. A volatility period of interest is calculated based upon the frequencies, and volatility thresholds are computed based upon the volatility period of interest. One or more periods of potential interest are detected in the dataset based upon the geospatial temporal data and the volatility thresholds. An indication of the periods of interest, an occurrence of an event captured in the dataset, or a prediction of an occurrence of an event that is of potential interest to an analyst is output.