论文标题
光谱传播图网络，用于几个时间序列分类
Spectral Propagation Graph Network for Few-shot Time Series Classification
论文作者
论文摘要
在时间序列分析中，很少有时间序列分类（几乎没有射击TSC）是一个具有挑战性的问题。当同一类的时间序列在光谱域不完全一致或不同类别的时间序列在频谱域中部分一致时，很难分类。为了解决这个问题，我们提出了一种名为Spectral Edgagation Graph网络（SPGN）的新方法，以明确建模并传播不同时间序列与图网络之间的光谱关系。据我们所知，SPGN是第一个在不同间隔中使用光谱比较的人，并涉及使用图形网络的所有时间序列的光谱传播。 SPGN首先使用带通滤波器在光谱域中扩展时间序列，以计算时间序列之间的光谱关系。 SPGN配备了图形网络，然后将光谱关系与标签信息集成在一起以进行光谱传播。进一步的研究传达了光谱关系获取和光谱传播之间的双向效应。我们在几乎没有TSC基准的基准上进行了广泛的实验。 SPGN的表现优于最先进的结果，$ 4 \％\ sim 13 \％$。此外，在跨域和跨路设置下，SPGN的价格分别超过了$ 12 \％$和$ 9 \％$。
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.
