ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
复制标题
DOI：
10.1007/s10618-020-00701-z
复制
发表时间：
2020-07-13
影响因子：
4.8
通讯作者：
Webb, Geoffrey, I
中科院分区：
文献类型：
--
作者：
Dempster, Angus;Petitjean, Francois;Webb, Geoffrey, I
大多数达到最先进精度的时间序列分类方法具有很高的计算复杂度，即使对于较小的数据集也需要大量的训练时间，并且对于较大的数据集来说是棘手的。此外，许多现有的方法专注于单一类型的特征，如形状或频率。基于最近卷积神经网络在时间序列分类中的成功，我们证明了使用随机卷积核的简单线性分类器可以实现最先进的准确性，而计算费用仅为现有方法的一小部分。使用这种方法，可以在不到2小时内在UCR存档中的所有85个“烘烤”数据集上训练和测试分类器，并且可以在大约1小时内在超过一百万个时间序列的大型数据集上训练分类器。
Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing methods focus on a single type of feature such as shape or frequency. Building on the recent success of convolutional neural networks for time series classification, we show that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods. Using this method, it is possible to train and test a classifier on all 85 'bake off' datasets in the UCR archive in < 2 h, and it is possible to train a classifier on a large dataset of more than one million time series in approximately 1 h.