2022
DOI: 10.48550/arxiv.2203.13652
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HYDRA: Competing convolutional kernels for fast and accurate time series classification

Abstract: We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time series using convolutional kernels, namely Rocket and its variants. We show that by adjusting a single hyperparameter it is possible to move by degrees between models resembling dictionary methods and models resembling Rocket. We present Hydra, a simple, fast, and accurate dictionary method for time ser… Show more

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Cited by 2 publications
(4 citation statements)
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“…Yet, convolution filters are found from the entire space of possible real-values. The most well known approach is ROCKET [5], with its successors MiniROCKET [6], MultiROCKET [32], and Hydra [7]. ROCKET generates tens of thousands of randomly parameterized convolutional kernels, and applies two pooling operations to the output: the maximum and the proportion of positive values.…”
Section: Time Series Classification (Tsc)mentioning
confidence: 99%
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“…Yet, convolution filters are found from the entire space of possible real-values. The most well known approach is ROCKET [5], with its successors MiniROCKET [6], MultiROCKET [32], and Hydra [7]. ROCKET generates tens of thousands of randomly parameterized convolutional kernels, and applies two pooling operations to the output: the maximum and the proportion of positive values.…”
Section: Time Series Classification (Tsc)mentioning
confidence: 99%
“…Dictionary (D): BOSS, cBOSS, WEASEL, TDE; Hybrid (H): HiveCote 2.0, HiveCote 1.0, TS_CHIEF, Deep-Learning (DL): InceptionTime; Shapelets (S): R-DST, MrSQM_SFA_k5; Kernel (K): Arsenal, MiniRocket, MultiRocket, Rocket, Hydra. We used implementations available in sktime [20], or published by the authors [7,12,17]. All reported numbers are accuracy on the test split.…”
Section: Competitorsmentioning
confidence: 99%
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