论文标题
Minirocket：时间序列分类非常快（几乎）确定性转换
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
论文作者
论文摘要
直到最近，时间序列分类的最准确方法受到高计算复杂性的限制。火箭通过使用随机卷积内核来转换输入时间序列，并使用转换的功能来训练线性分类器，从而通过大多数现有方法的计算费用的一小部分来实现最新的精度。我们将火箭重新制定为一种新方法Minirocket，在较大的数据集上最多使其更快地使其更快，并且使其几乎确定性（并且可以选择，并且具有额外的计算费用，完全确定性），同时保持基本相同的精度。使用此方法，可以在不到10分钟的时间内训练和测试从UCR存档到最新精度的所有109个数据集上的分类器。 Minirocket的速度明显比任何其他可比精度（包括火箭）的方法要快得多，并且比任何其他大致相似的计算费用的方法都要高得多。因此，我们建议现在应该将Minirocket视为火箭的默认变体。
Until recently, the most accurate methods for time series classification were limited by high computational complexity. ROCKET achieves state-of-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier. We reformulate ROCKET into a new method, MINIROCKET, making it up to 75 times faster on larger datasets, and making it almost deterministic (and optionally, with additional computational expense, fully deterministic), while maintaining essentially the same accuracy. Using this method, it is possible to train and test a classifier on all of 109 datasets from the UCR archive to state-of-the-art accuracy in less than 10 minutes. MINIROCKET is significantly faster than any other method of comparable accuracy (including ROCKET), and significantly more accurate than any other method of even roughly-similar computational expense. As such, we suggest that MINIROCKET should now be considered and used as the default variant of ROCKET.
