Deep neural networks (DNNs) that tackle the time series classification (TSC) task have provided a promising framework in signal processing. In real-world applications, as a data-driven model, DNNs are suffered from insufficient data. Few-shot learning has been studied to deal with this limitation. In this paper, we propose a novel few-shot learning framework through data augmentation, which involves transformation through the time-frequency domain and the generation of synthetic images through random erasing. Additionally, we develop a sequence-spectrogram neural network (SSNN). This neural network model composes of two sub-networks: one utilizing 1D residual blocks to extract features from the input sequence while the other one employing 2D residual blocks to extract features from the spectrogram representation. In the experiments, comparison studies of different existing DNN models with/without data augmentation are conducted on an amyotrophic lateral sclerosis (ALS) dataset and a wind turbine fault (WTF) dataset. The experimental results manifest that our proposed method achieves 93.75% F1 score and 93.33% accuracy on the ALS datasets while 95.48% F1 score and 95.59% accuracy on the WTF datasets. Our methodology demonstrates its applicability of addressing the few-shot problems for time series classification.

翻译：深度神经网络（DNNs）在处理时间序列分类（TSC）任务中为信号处理提供了有前景的框架。然而在实际应用中，作为数据驱动模型，DNNs面临数据不足的挑战。为应对这一局限性，少样本学习被广泛研究。本文提出一种新型少样本学习框架，通过数据增强实现，具体包括时频域变换和基于随机擦除的合成图像生成。此外，我们开发了序列-频谱图神经网络（SSNN）。该神经网络模型由两个子网络构成：一个采用一维残差模块从输入序列中提取特征，另一个则利用二维残差模块从频谱图表示中提取特征。在实验中，我们使用肌萎缩侧索硬化症（ALS）数据集和风力涡轮机故障（WTF）数据集，对比研究了不同现有DNN模型在有无数据增强条件下的表现。实验结果表明，所提方法在ALS数据集上达到93.75%的F1分数和93.33%的准确率，在WTF数据集上则获得95.48%的F1分数和95.59%的准确率。该方法的有效性证明了其在解决时间序列分类的少样本问题中的适用性。