Few-shot Time Series Classification (few-shot TSC) is a challenging problem in time series analysis. It is more difficult to classify when time series of the same class are not completely consistent in spectral domain or time series of different classes are partly consistent in spectral domain. To address this problem, we propose a novel method named Spectral Propagation Graph Network (SPGN) to explicitly model and propagate the spectrum-wise relations between different time series with graph network. To the best of our knowledge, SPGN is the first to utilize spectral comparisons in different intervals and involve spectral propagation across all time series with graph networks for few-shot TSC. SPGN first uses bandpass filter to expand time series in spectral domain for calculating spectrum-wise relations between time series. Equipped with graph networks, SPGN then integrates spectral relations with label information to make spectral propagation. The further study conveys the bi-directional effect between spectral relations acquisition and spectral propagation. We conduct extensive experiments on few-shot TSC benchmarks. SPGN outperforms state-of-the-art results by a large margin in $4\% \sim 13\%$. Moreover, SPGN surpasses them by around $12\%$ and $9\%$ under cross-domain and cross-way settings respectively.

提出了一个名为SPGN的新颖算法，它利用图网络显式建模和传播不同时间序列之间的频谱关系，结合标签信息进行谱传播，是少样本时间序列分类(SPGN)的第一个使用不同间隔的频谱比较和参与所有时间序列谱传播的方法。在少样本时间序列分类基准测试中，SPGN取得了4%至13%的大幅领先优势，并在跨领域和跨路径设置下超过了现有成果的约12%和9%。

Few-shot时间序列分类的频谱传播图网络