Real-Time Time-Series Signal Prediction Through Model Selection
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Solution Overview
Problem
Time series data from different domains exhibit considerable variations in properties, temporal scales, and dimensionality, limiting the ability to optimize machine learning models for accurate predictions due to data scarcity and the need for temporal segmentation, which conventional data augmentation techniques fail to address effectively.
Innovation Solution
The system employs empirically-optimized model selection, noise filtering using a Gaussian filter, and window size selection to enhance generalization ability, allowing optimal data predictions in real-time for time series data signals, even with small datasets, by optimizing a collection of base models and their parameters simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data augmentation techniques are applied to time series data, then data quantity increases, but temporal segmentation integrity is compromised and modeling results deteriorate
Solution Approach 1:
The patent creates synthetic time series data by copying and transforming existing data patterns through parameter variations. Instead of traditional augmentation that breaks temporal integrity, the system generates synthetic samples that preserve temporal segmentation by applying transformations that maintain the sequential structure and temporal relationships inherent in time series data, thereby increasing data quantity without compromising modeling results
2Measurement precision
If machine learning models are trained with limited domain-specific time series data, then model specialization improves, but optimization capability is constrained
Solution Approach 1:
The patent implements a universal model framework that can handle multiple domains and tasks through a single unified architecture. The system uses a base model collection that can be adapted to different time series domains without requiring separate specialized models for each application, thereby maintaining prediction accuracy while significantly improving model optimization capability and versatility across diverse domains
3Measurement precision
If multiple base models and parameters are optimized simultaneously, then prediction performance improves, but processing time increases
Solution Approach 1:
The patent performs preliminary optimization by pre-selecting and pre-training a collection of base models before actual prediction tasks. The system prepares an ensemble of candidate models with optimized parameters in advance, so that during real-time prediction, the system can quickly evaluate and combine these pre-optimized models without undergoing time-consuming optimization processes, thereby maintaining high prediction performance while reducing processing time
Data Source
AI summary
Methods and systems are disclosed for generating optimal data predictions in time series data signals based on empirically-optimized model selection, noise filtering, and window size selection using machine learning models. For example, the system may receive a first subset of time series data. The system may receive a prediction horizon. The system may generate a feature input based on the first subset of time series data and the prediction horizon. The system may input the feature input into a machine learning model, wherein the machine learning model includes multiple components. The system may receive an output from the machine learning model. The system may generate for display, on a user interface, a prediction for the first subset of time series data at the prediction horizon based on the output.