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Simulating Time-Series Data for Improved Deep Neural Network Performance
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DOI：10.1109/ACCESS.2019.2940701.png)
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摘要
En 中文
Deep learning algorithms have shown remarkable performance in classification tasks, however, they typically perform poorly with small training datasets due to overfitting. Overfitting occurs for all data types, although for the purposes of this study we are interested in time-based signals. This study introduces a novel technique to simulate time series signals from a dataset of categorically labeled data which can be used to train a deep neural network. The objective is to improve the predictive accuracy of a deep neural network on a separate validation dataset. To demonstrate the simulation methodology and improvements to the model's performance, a small dataset of ground reaction forces was used with the goal of identifying a person based on the raw signal. Our results show that the simulation method presented improves validation accuracy and reduces model training time for each of the three signal types.
Keyword:
Deep learning
deep neural networks
data simulation
data augmentation
time-series classification
time-series data augmentation
transfer learning
LSTM
1D CNN
ground reaction force
personal identification
small dataset
overfitting
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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