论文标题
HYDRA：快速准确的时间序列分类的竞争卷积内核
HYDRA: Competing convolutional kernels for fast and accurate time series classification
论文作者
论文摘要
我们演示了时间序列分类的字典方法之间的简单连接，该方法涉及提取和计算时间序列中的符号模式，以及基于使用卷积内核的转换输入时间序列的方法，即火箭及其变体。我们表明，通过调整单个高参数，可以在类似于字典方法和类似火箭的模型之间逐步移动。我们提出了使用竞争性卷积内核进行时间序列分类的简单，快速和准确的词典方法，结合了火箭和常规词典方法的关键方面。九头蛇比最准确的现有字典方法更快，更准确，并且可以与火箭及其变体结合使用，以进一步提高这些方法的准确性。
We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time series using convolutional kernels, namely ROCKET and its variants. We show that by adjusting a single hyperparameter it is possible to move by degrees between models resembling dictionary methods and models resembling ROCKET. We present HYDRA, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both ROCKET and conventional dictionary methods. HYDRA is faster and more accurate than the most accurate existing dictionary methods, and can be combined with ROCKET and its variants to further improve the accuracy of these methods.
