An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks (2007.15951v4)

Abstract: In recent times, deep artificial neural networks have achieved many successes in pattern recognition. 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. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

• The paper demonstrates that transformation-based methods and pattern mixing significantly boost neural network generalization in time series classification.
• It evaluates four augmentation families across 128 datasets and six neural architectures, detailing trade-offs in computational cost and accuracy.
• The study highlights the need for tailored data augmentation strategies to align with specific dataset characteristics and enhance model performance.

An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks

The paper "An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks" by Brian Kenji Iwana and Seiichi Uchida presents a comprehensive survey and empirical evaluation of various data augmentation techniques applied to time series classification using neural networks. The paper underscores the significance of data augmentation as a method to enhance generalization performance, particularly in domains where large datasets are not readily available. The paper systematically categorizes and evaluates these techniques across a spectrum of datasets and neural network architectures.

Overview of Data Augmentation Methods

The authors classify time series data augmentation methods into four primary families:

1. Transformation-based Methods: These involve direct transformations of the time series data such as jittering, rotation, scaling, and warping. The paper highlights the nuanced effectiveness of these methods, suggesting that their application requires careful parameter tuning to avoid inadvertently overlapping class boundaries or distorting the signal structure.
2. Pattern Mixing: This method combines multiple time series patterns to synthesize new data. Techniques like averaging, interpolation, and guided warping form part of this category. Pattern mixing helps preserve the intrinsic distribution of the original data by integrating features from multiple sources, which can be especially useful when class imbalance is a concern.
3. Generative Models: Models like GANs and variational autoencoders fall under this category, aiming to generate synthetic data that mirrors the real data's distribution. While powerful, these methods typically demand extensive training and resource allocation, making them less practical in scenarios where computational efficiency is paramount.
4. Decomposition Methods: These methods decompose time series into components, such as trends or independent factors, which can be analyzed and augmented separately. By focusing on structural components of time series data, these methods allow for more controlled data augmentation processes, potentially leading to robust model training.

Empirical Evaluation and Results

The empirical evaluation covers 128 diverse time series datasets using six different neural network models: MLP, VGG, ResNet, LSTM, BLSTM, and LSTM-FCN. This extensive approach allows the paper to draw generalized conclusions about the efficacy of augmentation methods across different types of data and neural architectures.

Key findings include:

• Transformation-based methods such as slicing and window warping typically yield positive results, especially in CNN-based architectures like VGG and ResNet. These methods, when carefully tuned, can enhance the model's ability to generalize by introducing variations that the networks can learn from.
• Pattern mixing and complex augmentation methods, though often computationally intensive, can provide significant performance gains. For instance, DGW, a guided warping method, stands out for its ability to adaptively combine features across time series, resulting in high accuracy improvements, especially for recurrent networks.
• Generative methods, while less frequently yielding direct performance gains in this paper, hold potential when paired with the right architectures and for specific tasks requiring high variability.

Implications and Future Directions

The paper provides a thorough baseline for researchers in time series analysis, detailing the trade-offs between computational complexity and augmentation method effectiveness. It emphasizes the need for augmentation strategy alignment with the data characteristics and underlying neural architecture. Practically, this work encourages domain practitioners to tailor their data augmentation processes, enhancing model performance without undue computational overhead.

As future developments in AI and machine learning ingest increasingly diverse datasets, the insights from this survey will become invaluable. Advanced techniques, possibly blending several of the methods discussed, could be developed to further optimize model training processes. This opens avenues for research not just into novel data augmentation strategies but also into adaptive frameworks that dynamically select and tune these methods based on real-time data characteristics and model feedback.

This paper stands as an essential roadmap for advancing time series classification, encouraging nuanced and methodical application of data augmentation to exploit the full potential of neural network models.