AI Model Selection via Time-Series Statistical Profiling
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Solution Overview
Problem
The implementation of artificial intelligence models, particularly those trained on time-series data, is hindered by the complexity and time-consuming nature of hyperparameter tuning and the need for large amounts of high-quality data, which often requires manual categorization and labeling.
Innovation Solution
The system automates model selection and hyperparameter optimization by using statistical tests to determine the time-series profile of a dataset, applying a profiling model to select the most effective model and hyperparameters based on dataset attributes, and filtering out models that are not suitable, thereby reducing redundant training and tuning efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple models are trained and tuned to determine the best fit for a dataset, then model selection accuracy is improved, but development time and computational resources are significantly increased
Solution Approach 1:
The patent applies preliminary action by performing statistical analysis on dataset attributes (such as seasonality, trends, and stationarity) before model training and selection. This preliminary characterization of the data allows the system to predict which models are likely to perform well, thereby avoiding the training of unsuitable models and reducing overall development time while maintaining selection accuracy.
2Reliability
If extensive hyperparameter tuning is performed to optimize model performance, then model performance is improved, but the complexity and time required for model development is increased
Solution Approach 1:
The system performs preliminary analysis of dataset attributes to identify characteristics such as seasonality, trends, and stationarity before hyperparameter tuning. This preliminary information guides the hyperparameter tuning process by providing informed starting points and constraints, reducing the search space and making the tuning process less complex while still achieving optimal model performance.
3Manufacturing precision
If manual data categorization and labeling are performed to ensure high data quality, then data quality is improved, but the time and labor required for data preparation is increased
Solution Approach 1:
The patent applies self-service by enabling the system to automatically characterize and analyze dataset attributes without requiring manual categorization or labeling. The statistical analysis routines automatically identify data patterns, seasonality, trends, and other characteristics, allowing the system to serve itself in terms of data understanding and model selection, thereby maintaining data quality while eliminating manual preparation time.
Data Source
AI summary
Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. The system may select a statistical profile type to identify in a first dataset. The system may retrieve a statistical model corresponding to the statistical profile type. The system may select, based on a first statistical profile, a first untrained model from a first plurality of untrained models for training, wherein the first plurality of untrained models comprises respective algorithms for time-series forecasting and wherein each of the first plurality of untrained models comprises default hyperparameter tuning. The system may, based on selecting the first untrained model, tune a first hyperparameter of the first untrained model using the first dataset.