  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.
