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Found 4 papers, 4 papers with code
HYDRA: Competing convolutional kernels for fast and accurate time series classification
We present HYDRA, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both ROCKET and conventional dictionary methods.
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art performance with a tiny fraction of the time and without the complex ensembling structure of many state-of-the-art methods.
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
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.
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
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.
