MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification
By Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb. Knowledge Discovery and Data Mining, 2020
545 citations recorded by the source corpus.
Rocket achieves state-of-the-art accuracy for time series classification 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. We reformulate Rocket into a new method, MiniRocket. MiniRocket is up to 75 times faster than Rocket on larger datasets, and almost deterministic (and optionally, fully deterministic), while maintaining essentially the same accuracy. Using this method, it is possible to train and test a classifier on all of 109 datasets from the UCR archive to state-of-the-art accuracy in under 10 minutes. MiniRocket is significantly faster than any other method of comparable accuracy (including Rocket), and significantly more accurate than any other method of remotely similar computational expense.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/bbae866c5edd477c1c39921436c47fd43d1f6e5c
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In Artificial Intelligence
- Carsten Eickhoff
- Anuradha Bhamidipaty
- George Zerveas
- Peter Christen
- Chenghao Liu
- Doyen Sahoo
- Gerald Woo
- Akshat Kumar
In Signal Processing
Citations in the corpus
- A Transformer-based Framework for Multivariate Time Series Representation Learning (2020, methodology): “Regression and classification of time series: Currently, nondeep learning methods such as TS-CHIEF [29], HIVE-COTE [21], and ROCKET [8, 9] constitute the state of the art for time series regression and classification based on evaluations on public benchmarks [2, 30], followed by CNN-based deep archi”