Hydra: competing convolutional kernels for fast and accurate time series classification
Hydra: competing convolutional kernels for fast and accurate time series classification
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Hydra：竞争卷积核，用于快速准确的时间序列分类
DOI：
10.1007/s10618-023-00939-3
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发表时间：
2022
影响因子：
4.8
通讯作者：
Geoffrey I. Webb
中科院分区：
文献类型：
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作者：
Angus Dempster;Daniel F. Schmidt;Geoffrey I. Webb
我们演示了时间序列分类的字典方法（涉及时间序列中的符号模式的提取和计数）与基于使用卷积核转换输入时间序列的方法（即 Rocket 及其变体）之间的简单联系。我们证明，通过调整单个超参数，可以在类似于字典方法的模型和类似于 Rocket 的模型之间逐步移动。我们提出了 Hydra，一种简单、快速且准确的字典方法，使用竞争卷积核进行时间序列分类，结合了 Rocket 和传统字典方法的关键方面。 Hydra 比现有最准确的字典方法更快、更准确，其准确度与当前几种最准确的时间序列分类方法相似。 Hydra 还可以与 Rocket 及其变体结合使用，以显着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, achieving similar accuracy to several of the most accurate current methods for time series classification. Hydra can also be combined with Rocket and its variants to significantly improve the accuracy of these methods.
DOI：
10.3233/ida-184333
发表时间：
2019-10
期刊：
Intell. Data Anal.
影响因子：
--
作者：
J. Large;A. Bagnall;S. Malinowski;R. Tavenard
通讯作者：
J. Large;A. Bagnall;S. Malinowski;R. Tavenard