Recent works have demonstrated the superiority of supervised Convolutional
Neural Networks (CNNs) in learning hierarchical representations from time
series data for successful classification. These methods require sufficiently
large labeled data for stable learning, however acquiring high-quality labeled
time series data can be costly and potentially infeasible. Generative
Adversarial Networks (GANs) have achieved great success in enhancing
unsupervised and semi-supervised learning. Nonetheless, to our best knowledge,
it remains unclear how effectively GANs can serve as a general-purpose solution
to learn representations for time series recognition, i.e., classification and
clustering. The above considerations inspire us to introduce a Time-series
Convolutional GAN (TCGAN). TCGAN learns by playing an adversarial game between
two one-dimensional CNNs (i.e., a generator and a discriminator) in the absence
of label information. Parts of the trained TCGAN are then reused to construct a
representation encoder to empower linear recognition methods. We conducted
comprehensive experiments on synthetic and real-world datasets. The results
demonstrate that TCGAN is faster and more accurate than existing time-series
GANs. The learned representations enable simple classification and clustering
methods to achieve superior and stable performance. Furthermore, TCGAN retains
high efficacy in scenarios with few-labeled and imbalanced-labeled data. Our
work provides a promising path to effectively utilize abundant unlabeled time
series data.

一篇最近的研究表明，在学习成功分类的时间序列数据的分层表示方面，监督卷积神经网络（CNNs）具有优势。然而，这些方法需要足够大规模的标记数据进行稳定学习，然而获取高质量的标记时间序列数据可能是昂贵且难以实现的。因此，本文引入了一种时间序列卷积生成对抗网络（TCGAN），通过在没有标记信息的情况下，让两个一维 CNN（即生成器和判别器）进行对抗性博弈来学习。TCGAN 在合成和真实数据集上进行了广泛的实验证明其比现有时间序列生成对抗网络更快且更准确，其学习到的表示使得简单的分类和聚类方法能够达到优越且稳定的性能，在少标记和不平衡标记的场景下仍保持高效性。本研究为有效利用丰富的无标记时间序列数据提供了一条有希望的途径。

TCGAN：卷积生成对抗网络用于时间序列分类和聚类

TCGAN: Convolutional Generative Adversarial Network for Time Series  Classification and Clustering

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.

本论文调研了时间序列数据增强技术及其在神经网络时间序列分类中的应用，其中提出了四种基于变换、模式混合、生成模型和分解方法的方法， 并在 128 个时间序列分类数据集上使用六种不同类型的神经网络对 12 种时间序列数据增强方法进行了实证评估，以分析每种数据增强方法的特点、优缺点和建议，并旨在帮助选择神经网络应用的时间序列数据增强方法