Search Results for author: Angus Dempster

Found 4 papers, 4 papers with code

HYDRA: Competing convolutional kernels for fast and accurate time series classification

1 code implementation25 Mar 2022 Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb

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.

Time Series Time Series Classification

MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification

1 code implementation31 Jan 2021 Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I. Webb

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.

General Classification Time Series +1

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

2 code implementations16 Dec 2020 Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb

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.

General Classification Time Series +1

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

5 code implementations29 Oct 2019 Angus Dempster, François Petitjean, Geoffrey I. Webb

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.

Classification General Classification +2

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