ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
This addresses the need for faster and more scalable time series classification, which is crucial for applications like finance and healthcare, though it is incremental as it builds on existing convolutional neural network successes.
The paper tackles the problem of high computational complexity in state-of-the-art time series classification methods by proposing a simple approach using random convolutional kernels with linear classifiers, achieving comparable accuracy at a fraction of the computational expense.
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.