Learning to classify time series with limited data is a practical yet
challenging problem. Current methods are primarily based on hand-designed
feature extraction rules or domain-specific data augmentation. Motivated by the
advances in deep speech processing models and the fact that voice data are
univariate temporal signals, in this paper, we propose Voice2Series (V2S), a
novel end-to-end approach that reprograms acoustic models for time series
classification, through input transformation learning and output label mapping.
Leveraging the representation learning power of a large-scale pre-trained
speech processing model, on 30 different time series tasks we show that V2S
performs competitive results on 19 time series classification tasks. We further
provide a theoretical justification of V2S by proving its population risk is
upper bounded by the source risk and a Wasserstein distance accounting for
feature alignment via reprogramming. Our results offer new and effective means
to time series classification.

本研究提出了一种名为 Voice2Series 的端到端方法，通过输入转换学习和输出标签映射将声学模型重新编程用于时间序列分类，展示了在 30 个不同的时间序列任务中 V2S 在 19 个任务上表现出有竞争力的结果，同时还提供了关于其人群风险的理论证明。