time-series-classification

ChorusTIC is a training-free foundation model for multivariate time series classification that uses in-context learning to handle heterogeneous channel configurations without target-task updates, demonstrating strong performance on standard benchmarks.

This paper evaluates SelF-Rocket for multi-class fault diagnosis in electrical and mechanical systems, introducing a multivariate extension and comparing it with ROCKET-based methods on benchmark datasets.

This paper introduces drXAI, a method that uses XAI attribution to reduce data size for time series classification, achieving 80-90% data reduction while maintaining accuracy, enabling large models to scale.

This paper shows that traditional machine learning models using spectral features from EEG signals can match or outperform state-of-the-art attention-based deep learning models for diagnosing neurodegenerative diseases, suggesting fundamental limitations of attention mechanisms in this domain.