Automated Self-Join Feature Discovery for Delayed Time-Based Effects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to efficiently and accurately model complex systems by capturing delayed effects of time-based features, leading to lower accuracy and precision in predictive models.
Innovation Solution
A system and method that utilize self-join operations to identify and generate features with time-based characteristics, aggregating data sets to create composite features, and update models via machine learning to enhance predictive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to capture time-based features, then model accuracy can be improved, but productivity and speed of analysis deteriorate
Solution Approach 1:
The system performs automated self-join operations on time-series data to capture delayed effects of time-based features. The automated feature discovery process enables the system to service itself by automatically identifying and creating lagged features without manual intervention, thereby maintaining high model accuracy while significantly improving productivity and analysis speed.
2Manufacturing precision
If complex self-join operations are automated to capture delayed effects, then model precision is improved, but device complexity increases
Solution Approach 1:
The system introduces an intermediary automated feature discovery layer that mediates between raw time-series data and the predictive model. This intermediary automatically performs complex self-join operations to create lagged features, thereby improving model precision while shielding the user from the underlying complexity of the operations.
Solution Approach 2:
The system replaces manual mechanical processes of feature engineering with automated computational operations. The automated feature discovery process substitutes manual data manipulation with algorithmic self-join operations, improving model precision while reducing the need for manual intervention and simplifying the user experience.
3Loss of information
If comprehensive feature extraction is performed to capture delayed effects, then information completeness is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary automated self-join operations to pre-capture delayed effects of time-based features before model training. By proactively creating lagged features and capturing delayed effects in advance, the system ensures information completeness is maintained while reducing the time required during actual model training and deployment.
Data Source
AI summary
Aspects of this technical solution can generate, according to a lag time window based at least in part on a first plurality of features, a second data set via aggregation of compatible fields in the first data set, the first plurality of features corresponding to a first data set, augment the first plurality of features extracted from the first data set with a second plurality of features extracted from a third data set, the third data set corresponding to a join of the first data set and the second data set, update, via machine learning and according to a rate corresponding to the data set, a model with the third plurality of features, and instruct a user interface to present at least one performance of the model with the third plurality of features, according to the rate.