Neural ODE Sensor Data Training for Long-Term Time-Series Prediction
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Solution Overview
Problem
Training neural networks to model long-term sensor data dynamics is challenging due to the contrast between efficient training using mean squared deviation and batch parallelization with the quality criterion of good, long-term prediction.
Innovation Solution
A method involving training a neural network to model an ordinary differential equation by progressively increasing the length of training segments in batches, using multiple-shooting methods to enhance prediction accuracy, and implementing termination criteria based on predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training datasets with increasingly longer segments are used to improve long-term prediction quality, then the training efficiency deteriorates due to increased computational complexity
Solution Approach 1:
The training process is segmented into multiple stages with progressively increasing segment lengths. The patent divides training into epochs where each epoch uses segments of a specific length, gradually increasing from short to long segments. This allows the neural network to first learn local patterns efficiently, then progressively capture long-term dynamics without being overwhelmed by computational complexity in a single training run.
Solution Approach 2:
The patent performs preliminary training with shorter segments before advancing to longer segments. Each epoch serves as a preliminary step that prepares the network for the next epoch with longer segments. This preliminary action at each stage builds a foundation that makes subsequent training with more demanding data feasible and efficient.
2Productivity
If batch parallelization is used to improve training efficiency, then the quality of long-term prediction deteriorates due to loss of temporal continuity
Solution Approach 1:
The patent dynamically adjusts the segment length used in training batches across different epochs. Rather than using fixed-length segments throughout training, the system evolves the segment length parameter, increasing it as training progresses. This dynamic approach allows batch parallelization to maintain efficiency while progressively capturing longer-term temporal dependencies that improve prediction quality.
3Measurement precision
If the neural network is trained on the entire time series to improve long-term dynamics modeling, then the training time increases significantly
Solution Approach 1:
The patent implements periodic training epochs with different segment lengths. Instead of continuously training on the entire time series, the system periodically cycles through epochs with increasing segment lengths. This periodic structure allows the network to repeatedly refine its understanding at different temporal scales, achieving comprehensive long-term dynamics modeling through repeated exposure to progressively longer segments rather than continuous training on all data.
Data Source
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AI summary
A device and a computer-implemented method for providing sensor data, wherein a neural network models an ordinary differential equation for providing sensor data, provides that the sensor data are provided in a time series (200), wherein training data sets with increasingly longer segments (202) are determined from the time series (200) from the sensor data, and the neural network is trained with the training data sets with increasing length of the segments (202) to predict a solution of the ordinary differential equation.