Slope Features for Temporally Spaced Data Analysis
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Solution Overview
Problem
Existing data analysis models relying primarily on aggregate features often miss time-based trends in temporally spaced data, leading to less accurate predictions.
Innovation Solution
Incorporating slope features, such as first and higher-order derivatives, into machine learning algorithms to capture time-based trends by defining time windows and applying these features to datasets, allowing for deeper analysis and more precise predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If aggregate features are used in data analysis models, then the model structure remains simple, but time-based trends in temporally spaced data are missed leading to less accurate predictions
Solution Approach 1:
The patent segments the temporal data into multiple time windows (e.g., recent, intermediate, historical periods) and calculates separate aggregate features for each window. This segmentation allows the model to capture time-based trends by comparing features across different time periods while maintaining a structured and manageable feature engineering process.
Solution Approach 2:
The patent introduces dynamic features that capture temporal relationships, such as rate of change features (difference between recent and historical averages), trend features (direction and magnitude of change over time), and temporal pattern features. These dynamic features enable the model to adapt to changing patterns in the data while maintaining computational efficiency.
2Measurement precision
If more time-dependent features are added to capture temporal trends, then prediction accuracy improves, but the complexity of feature engineering increases
Solution Approach 1:
The patent systematically segments temporal data into distinct time windows and applies consistent feature transformation rules to each segment. This structured approach allows for comprehensive temporal analysis without creating an unmanageable number of features, as the same segmentation logic can be applied recursively to different time scales.
Solution Approach 2:
The patent implements a hierarchical feature engineering approach where essential temporal features are calculated first (e.g., basic aggregates per time window), and additional sophisticated features (e.g., higher-order derivatives, complex temporal patterns) are added only when they provide measurable improvement. This partial action principle allows the model to start simple and progressively incorporate more complex features as needed.
3Measurement precision
If comprehensive temporal analysis is performed using multiple time windows and derivatives, then the ability to capture time-based trends improves, but computational requirements increase
Solution Approach 1:
The patent divides the computational task into segments by processing different time windows independently and applying the same transformation functions repeatedly. This segmentation allows for efficient parallelization and optimization, as each time window can be processed separately using cached base features, reducing redundant computations.
Solution Approach 2:
The patent pre-calculates base aggregate features (such as sums, means, and counts) for each time window before applying derivative transformations. These preliminary computations are stored and reused across multiple feature derivations, significantly reducing computational energy requirements when calculating higher-order features like rates of change or trends.
Data Source
AI summary
Techniques for the implantation of time-dependent features (e.g., slope features) in existing data analysis models are disclosed. Time-dependent features are applied in machine learning algorithms to provide deeper analysis of temporally spaced data. Temporally spaced data is time-based or time-dependent data where data is populated at different points in time over some period of time. Implementing the time-dependent features enables application of first derivatives that define slopes over time (e.g., performance) windows within the period of time of the data. Application of the first derivatives provides analysis of the trend of the data over time. Additional features and/or higher order derivatives may also be applied to the first derivatives to provide further refinement to analysis of the temporally spaced data.