Meta-Learning System for Automatic Time-Series Forecasting Model Selection
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Solution Overview
Problem
Existing time-series forecasting models require extensive manual effort and time to construct and evaluate, making it impractical to select the best model for new datasets, especially since many models need to be tested across various hyperparameters, and current meta-learning systems struggle to select models outside their domain or across different time windows.
Innovation Solution
A meta-learning system that extracts meta-features from time-series datasets and uses a trained meta-learner machine learning model to select the forecasting model with the highest predicted performance, learning relationships between meta-features and model performances across multiple datasets, allowing for automatic selection without prior domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple forecasting models are trained and tested on a new dataset to determine the best performing model, then model selection accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-training multiple forecasting models on historical datasets before actual deployment. When a new dataset arrives, the pre-trained models can be quickly evaluated without extensive training, significantly reducing time consumption while maintaining selection accuracy.
Solution Approach 2:
The system creates copies of forecasting models with different architectures and parameters. Instead of training one model extensively, multiple model copies are prepared in advance with varying configurations, allowing rapid comparison and selection on new datasets without repeating the full training process for each candidate.
2Measurement precision
If experts manually construct forecasting models with customized architecture and features for specific tasks, then model performance is improved, but device complexity and manual effort increase
Solution Approach 1:
The system develops universal forecasting models that can handle multiple types of time-series tasks and datasets. Instead of requiring expert-manual construction for each specific task, these multi-functional models can be adapted to different domains (weather, finance, energy) through configuration rather than re-construction, reducing complexity while maintaining performance.
Solution Approach 2:
The system enables automated model construction and selection that performs the work previously requiring expert intervention. The automated framework evaluates datasets, selects appropriate models, and configures parameters without human involvement, reducing both manual effort and the apparent complexity of model construction while achieving expert-level performance.
3Measurement precision
If meta-learning systems are trained on specific domains, then domain accuracy is improved, but adaptability to new domains decreases
Solution Approach 1:
The meta-learning system is designed with universal components that can adapt to multiple domains. By training on diverse historical datasets from different domains and using domain-agnostic feature extraction, the system maintains high accuracy across weather, finance, energy, and other domains without requiring domain-specific retraining, thus achieving both accuracy and adaptability.
Solution Approach 2:
The system adjusts parameters dynamically based on the input dataset characteristics rather than being fixed to specific domain parameters. By changing parameters adaptively based on data patterns rather than domain labels, the system maintains accuracy across domains while improving adaptability to new domains without extensive retraining.
Data Source
AI summary
Systems and methods for automatic forecasting are described. Embodiments of the present disclosure receive a time-series dataset; compute a time-series meta-feature vector based on the time-series dataset; generate a performance score for a forecasting model using a meta-learner machine learning model that takes the time-series meta-feature vector as input; select the forecasting model from a plurality of forecasting models based on the performance score; and generate predicted time-series data based on the time-series dataset using the selected forecasting model.