_“The task of time series classification can be thought of as involving learning or detecting signals or patterns within time series associated with relevant classes.” — _Dempster, et al 2020, authors of ROCKET paper

Most time series classification methods with state-of-the-art (SOTA) accuracy have high computational complexity and scale poorly. This means they are slow to train on smaller datasets and effectively unusable on large datasets.

_ROCKET (RandOM Convolutional KErnal Transform) can achieve the same level of accuracy in just a fraction of the time as competing SOTA algorithms, including convolutional neural networks. The algorithms were evaluated on the benchmark datasets in the _UCR Archive.

ROCKET first transforms the time series dataset using random convolutional kernels, such as those used in a CNN, and then trains a linear classifier with these features.

How much faster is ROCKET? To train and test ROCKET on 85 benchmark datasets sequentially, it took 1 hour 40 min. For the same task, the next fastest SOTA algorithm (cBOSS) took 19 hours 33 minutes. For more comparisons on speed, see the paper.

In the remainder of this article, I will:

• Discuss alternative time series classifiers
• Explain how ROCKET works
• Provide a python code example

#data-science #machine-learning #python #artificial-intelligence #time-series-analysis