Futures Margin Modeling with Wavelet Seasonality Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently detecting seasonality in energy products for accurate margin and collateral modeling, leading to computationally intensive and time-consuming processes that fail to meet regulatory time limits and market participant expectations.
Innovation Solution
A computing system utilizing a combination of wavelet techniques and rolling techniques to efficiently detect seasonality in energy products, enabling more accurate margin and collateral modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seasonality detection methods are used for energy products, then comprehensive analysis can be performed, but the process becomes computationally intensive and time-consuming
Solution Approach 1:
The patent segments the complex seasonality detection process into multiple filter stages (bandpass filter, highpass filter, lowpass filter) that process different frequency components separately. This segmentation allows the system to analyze seasonal patterns at various time scales efficiently without requiring a single computationally intensive comprehensive analysis, thereby reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The patent changes the parameter of filter application by applying different types of filters (bandpass, highpass, lowpass) with specific parameters to detect seasonality at different frequencies. By adjusting filter parameters dynamically based on the data characteristics, the system achieves accurate seasonality detection without requiring exhaustive computational analysis of all possible patterns.
2Measurement precision
If comprehensive seasonality analysis is performed on all energy products, then accurate margin calculations can be achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent segments the computational workload by dividing seasonality detection into multiple filter stages that process different frequency components independently. This segmentation allows the system to focus computational resources on specific seasonal patterns rather than performing exhaustive analysis of all possible seasonal variations, thereby reducing overall computational energy consumption while maintaining margin calculation accuracy.
Solution Approach 2:
The patent applies different filter types (bandpass, highpass, lowpass) to different frequency components of the price data, creating local quality variations in the analysis approach. Each filter stage is optimized for detecting specific seasonal patterns at particular frequencies, allowing the system to achieve accurate margin calculations by applying appropriate analysis methods locally rather than using a uniform computationally intensive approach across all data.
3Reliability
If traditional methods are used to identify seasonal products, then thorough analysis can be conducted, but the process takes weeks and months
Solution Approach 1:
The patent segments the seasonality identification process into parallel filter stages that can be executed simultaneously rather than sequentially. By dividing the analysis into bandpass, highpass, and lowpass filter components that operate independently, the system achieves thorough seasonal pattern recognition with much faster processing speeds, transforming a process that traditionally took weeks or months into one that completes in minutes or hours.
Solution Approach 2:
The patent employs periodic filter operations that systematically analyze different frequency components at regular intervals. This periodic action allows the system to efficiently scan through potential seasonal patterns using standardized filter applications, maintaining reliable seasonality identification while dramatically improving processing productivity compared to traditional comprehensive analysis methods.
Data Source
AI summary
A physical container (e.g., a battery) may be filled up (charged) or emptied (discharged) with energy commensurate with requirements to post a particular amount of collateral. The disclosure provides computing systems and methods for processing data using a novel combination of wavelet techniques and rolling techniques to more efficiently detect seasonality in particular products (e.g., energy products) to more accurately model and determine collateral/margin requirements. A clearinghouse computing device may be configured to generate a margin requirement for a portfolio of products and may include a processor to process instructions that cause the clearinghouse computing device to perform wavelet decomposition and rolling methods on a historical database of records.