EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
Highly comparative feature-based time-series classification
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ERIS combines energy-guided calibration, weight-level orthogonality, and auxiliary adversarial generalization to produce shift-robust representations for out-of-distribution time series classification.
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ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification
ERIS combines energy-guided calibration, weight-level orthogonality, and auxiliary adversarial generalization to produce shift-robust representations for out-of-distribution time series classification.