Neural Stream Interpolation for Multi-Rate Data Gap Filling
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Solution Overview
Problem
Existing systems struggle to effectively interpolate data streams from multiple sources with varying sampling rates and sensitivities, leading to inefficiencies and gaps in data availability.
Innovation Solution
A predictive model using an encoder-decoder architecture with neural networks is trained to synchronize and interpolate data streams by minimizing loss functions through backpropagation, ensuring high-quality reconstruction and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data interpolation methods are used, then data gaps can be filled, but the quality of interpolated data deteriorates and reliability is reduced
Solution Approach 1:
The patent replaces traditional mechanical/mathematical interpolation methods with a neural network-based system. The encoder-decoder architecture with embedding layers and loss function optimization (MSE, L1, Huber) enables the system to learn complex patterns and relationships in time series data, producing high-quality interpolated values that maintain reliability while effectively filling data gaps.
2Productivity
If multiple data streams with varying sampling rates are processed, then data availability is improved, but system complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming data streams with different sampling rates into a unified embedding space through the encoder network. The model learns to handle varying temporal resolutions by adjusting the embedding representation, allowing the system to process multiple data streams with different sampling rates without requiring complex resampling or synchronization mechanisms.
Solution Approach 2:
The encoder-decoder architecture provides universality by serving multiple functions: it encodes data streams with different sampling rates into embeddings, decodes them back to unified time grids, handles missing data, and performs interpolation. This multi-functional approach consolidates what would otherwise require multiple separate processing systems into a single unified model.
3Measurement precision
If neural network models are trained extensively, then interpolation accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by using a limited set of loss functions (MSE, L1, Huber) rather than exhaustive optimization, and by training the model to handle only the essential patterns needed for interpolation. The encoder-decoder architecture with embedding layers provides a efficient representation that captures key temporal dependencies without requiring excessive training data or computational resources, achieving good accuracy with moderate training effort.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: applying a first data stream to a first encoder network that produces a first embedding of the first data stream and inputting the first embedding to a first decoder network that produces a first reconstructed stream of the first data stream; inputting a second data stream to a second encoder network that produces a second embedding of the second data stream and inputting the second embedding to a second decoder network that produces a second reconstructed stream of the second data stream, wherein the first encoder network, the first decoder network, the second encoder network, and the second decoder network define a predictive model; and determining an embedding loss function between the first embedding and the second embedding.