ROCKET(RandOm Convolutional KErnel Transform) ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels 论文:arxiv.org/abs/1910.1305 github:github.com/angus924/roc 理论知识
Rocket (RandOm Convolutional KErnel Transform)利用随机卷积核将时序数据进行变换,然后变换的特征训练一个线性分类器。 在UCR archive 数据集上sota,训练时间变少。 85个数据集上总共计算时间为1h40min,而cBOSS方法需要19h33min,而InceptionTime网络需要6days。 [Method]Rocket使用大量随机卷积核变换时间序列,这里的随机...
ROCKET(RandOm Convolutional KErnel Transform)是一种基于随机卷积核变换的时间序列分类方法。以下是一个简单的ROCKET分类算法实例: 1.数据预处理:将时间序列数据预处理成适合进行特征提取的形式。例如,可以使用数据清洗技术去除异常值和噪声,或者进行特征工程,如计算时间序列的统计量、小波变换等。 2.随机生成卷积核:ROCK...
The second set is derived from the ROCKET (RandOm Convolutional KErnel Transform) algorithm [22], where 20,000 features are extracted and subsequently reduced to 87 principal components using PCA. This comparative analysis within the XGBoost framework will advance our understanding of attachment style ...
We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we ...
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels - angus924/rocket
random.random(size=(10, 1, 100)).astype(np.float32) r = MiniRocket(random_state=0) p1 = r.fit_transform(X) r2 = MiniRocket(random_state=0) p2= r2.fit_transform(X) r3 =BadPlaceMiniRocket(random_state=0) r4 = BadPlaceMiniRocketMultivariate(random_state=0) p3= r3.fit_transform(...
我们称这种方法为Rocket(RandOm Convolutional KErnel Transform) 现有的时间序列分类方法通常集中于单一的表示,如形状、频率或方差。卷积核构成了一个单一的机制,可以捕获许多以前需要各自专门技术的特征,并已被证明在卷积神经网络的时间序列分类中是有效的,如ResNet (Wang et al. 2017;Ismail Fawaz et al. 2019a)...
Therefore, this study aims to introduce a method for enhancing time series classification performance by integrating MINImally RandOm Convolutional KErnel Transform (MiniRocket) and a genetic algorithm (GA)-based ensemble method for time series classification in the context of predictive maintenance. This...
提出一种方法,称为Rocket (RandOm Convolutional KErnel Transform)。该方法利用随机卷积核将时序数据首先进行变换,然后变换的特征训练一个线性分类器。这种方法首先在UCR archive 数据集上取得了最好的效果,其次需要的训练时间也比现有的方法更少。在85个数据集上的总共计算时间为1h40min,而cBOSS方法需要19h33min,而...