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

arXiv: https://arxiv.org/abs/2012.08791 · HTML: https://mlsanity.com/paper/2012.08791/

## Key facts

- Cited by 190 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 30 of 190)

- [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://mlsanity.com/paper/2308.08241/) (2023)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification](https://mlsanity.com/paper/2102.00457/) (2021)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022)
- [A Survey on Time-Series Pre-Trained Models](https://mlsanity.com/paper/2305.10716/) (2023)
- [LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version](https://mlsanity.com/paper/2302.12721/) (2023)
- [Improving Position Encoding of Transformers for Multivariate Time Series Classification](https://mlsanity.com/paper/2305.16642/) (2023)
- [Out-of-Distribution Representation Learning for Time Series Classification](https://mlsanity.com/paper/2209.07027/) (2022)
- [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023)
- [ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis](https://mlsanity.com/paper/2403.01493/) (2024)
- [MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2306.13102/) (2023)
- [BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning](https://mlsanity.com/paper/2306.13101/) (2023)
- [DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization](https://mlsanity.com/paper/2308.02282/) (2023)
- [Dynamic Sparse Network for Time Series Classification: Learning What to "see''](https://mlsanity.com/paper/2212.09840/) (2022)
- [Uncertainty-DTW for Time Series and Sequences](https://mlsanity.com/paper/2211.00005/) (2022)
- [FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification](https://mlsanity.com/paper/2302.09818/) (2023)
- [T-Rep: Representation Learning for Time Series using Time-Embeddings](https://mlsanity.com/paper/2310.04486/) (2023)
- [Brant-X: A Unified Physiological Signal Alignment Framework](https://mlsanity.com/paper/2409.00122/) (2024)
- [WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification](https://mlsanity.com/paper/2301.10194/) (2023)
- [QUANT: A Minimalist Interval Method for Time Series Classification](https://mlsanity.com/paper/2308.00928/) (2023)
- [Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data](https://mlsanity.com/paper/2410.04814/) (2024)
- [An Efficient Federated Distillation Learning System for Multi-task Time Series Classification](https://mlsanity.com/paper/2201.00011/) (2021)
- [HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing](https://mlsanity.com/paper/2202.08055/) (2022)
- [ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification](https://mlsanity.com/paper/2405.14608/) (2024)
- [Set Features for Fine-grained Anomaly Detection](https://mlsanity.com/paper/2302.12245/) (2023)
- [A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids](https://mlsanity.com/paper/2110.06324/) (2021)
- [A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis](https://mlsanity.com/paper/2310.11959/) (2023)
- [Advancing Time Series Classification with Multimodal Language Modeling](https://mlsanity.com/paper/2403.12371/) (2024)
- [TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification](https://mlsanity.com/paper/2406.04419/) (2024)

## Abstract (arXiv)

Until recently, the most accurate methods for time series classification were limited by high computational complexity. 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. We reformulate ROCKET into a new method, MINIROCKET, making it up to 75 times faster on larger datasets, and making it almost deterministic (and optionally, with additional computational expense, 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 less than 10 minutes. MINIROCKET is significantly faster than any other method of comparable accuracy (including ROCKET), and significantly more accurate than any other method of even roughly-similar computational expense. As such, we suggest that MINIROCKET should now be considered and used as the default variant of ROCKET.