We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, under certain settings, are invariant to time-warping. We are competitive with state-of-the-art methods on the UCR archive, both in terms of accuracy and speed. We make our code available at \url{https://github.com/irkri/fruits}.

翻译：我们提出了一种用于时间序列分类的流程，该方法基于迭代累加和签名（ISS）提取特征，随后应用线性分类器。这些特征本质上是非线性的，能够捕捉时序信息，并且在特定设置下对时间扭曲具有不变性。在UCR数据集上，我们的方法在准确率和速度方面均与现有最优方法水平相当。我们的代码已开源至 \url{https://github.com/irkri/fruits}。