An empirical survey of data augmentation for time series classification with neural networks

🤖 Plain-English Summary

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method.

🔑 Key Findings

• Part of this success can be attributed to the reliance on big data to increase generalization.
• However, in the field of time series recognition, many datasets are often very small.
• One method of addressing this problem is through the use of data augmentation.

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