Data to 14 Oct 2026

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

arXiv 1910.13051 ↗First posted 29 Oct 2019cs.LG stat.MLcited by 339

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Abstract (arXiv)

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. Additionally, many existing methods focus on a single type of feature such as shape or frequency. Building on the recent success of convolutional neural networks for time series classification, we show that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods.

Key facts. Cited by 339 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Oct 2019.

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Citing papers per year: 2021: 24; 2022: 38; 2023: 68; 2024: 76; 2025: 70; 2026: 63.

339 papers.

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2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 250
2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 116
2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 103
2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 101
2022Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusioncited by 96
2024UniTS: A Unified Multi-Task Time Series Modelcited by 94
2021MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcited by 90
2021Chaos as an interpretable benchmark for forecasting and data-driven modellingcited by 88

38 more

2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Seriescited by 79
2024TSLANet: Rethinking Transformers for Time Series Representation Learningcited by 74
2022Flowformer: Linearizing Transformers with Conservation Flowscited by 69
2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 54
2023TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learningcited by 52
2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 50
2024aeon: a Python toolkit for learning from time seriescited by 47
2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 45
2025Context-Alignment: Activating and Enhancing LLM Capabilities in Time Seriescited by 39
2023Cross-Modal Fine-Tuning: Align then Refinecited by 34
2021NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Taskscited by 32
2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 29
2023Model scale versus domain knowledge in statistical forecasting of chaotic systemscited by 27
2021The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classificationcited by 26
2023MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signalscited by 26
2024UniMTS: Unified Pre-training for Motion Time Seriescited by 26
2023BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learningcited by 25
2024Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learningcited by 25
2025Mantis: Lightweight Foundation Model for Time Series Classificationcited by 24
2023Inherently Interpretable Time Series Classification via Multiple Instance Learningcited by 23
2021Random Dilated Shapelet Transform: A New Approach for Time Series Shapeletscited by 22
2022Dynamic Sparse Network for Time Series Classification: Learning What to "see''cited by 22
2024RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Taskscited by 22
2022ClaSP -- Parameter-free Time Series Segmentationcited by 21
2023FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classificationcited by 21
2023NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretrainingcited by 20
2026Beyond IID: How General Are Tabular Foundation Models, Really?cited by 20
2024xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memoriescited by 19
2022The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifiercited by 18
2022Efficient Architecture Search for Diverse Taskscited by 18
2024RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Datacited by 17
2025TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecastingcited by 17
2025PPGF: Probability Pattern-Guided Time Series Forecastingcited by 16
2023WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classificationcited by 15
2023Interpretation of Time-Series Deep Models: A Surveycited by 15
2023QUANT: A Minimalist Interval Method for Time Series Classificationcited by 15
2024Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Datacited by 15

50 most cited of 339 All 339 (JSON)