Time series classification with random convolution kernels based transforms: pooling operators and input representations matter
Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier
Abstract
This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC). Unlike existing approaches based on random convolution kernels, it dynamically selects the best couple of input representations and pooling operator during the training process. SelF-Rocket achieves state-of-the-art accuracy on the University of California Riverside (UCR) TSC benchmark datasets.
Related Papers
mNARX+: A surrogate model for complex dynamical systems using manifold-NARX and automatic feature selection2025-07-17MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling2025-07-17The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting2025-07-17Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection2025-07-15Data Augmentation in Time Series Forecasting through Inverted Framework2025-07-15D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data2025-07-15Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning2025-07-14Towards Interpretable Time Series Foundation Models2025-07-10