  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.
