Crossfire: cross-domain feature integration for robust time series classification

Feature-based time series classification (TSC) methods have traditionally relied on time-domain features, which can limit their effectiveness in capturing the full spectrum of temporal dynamics. This study introduces Crossfire (Cross-representation Feature Integration for Robust Extraction, CFIRE), a feature-based framework that systematically extracts and integrates features from a diverse set of time series representations—including derivative, autocorrelation, spectral (Fourier), harmonic (cosine), time-frequency (wavelet), and analytic (Hilbert) domains. The pipeline supports parallelized feature extraction and incorporates selection mechanisms to manage redundancy while enhancing classification performance. Empirical evaluation across all 142 datasets in the UCR archive demonstrates that CFIRE outperforms leading feature-based baselines such as FreshPRINCE. Although it does not always surpass state-of-the-art (SOTA) accuracy in other TSC paradigms, CFIRE provides distinctive advantages in computational efficiency, interpretability, and scalability. Notably, its computational efficiency is comparable to that of Quant, one of the fastest existing TSC methods, while maintaining robust accuracy across longer sequences and larger class counts. The results indicate that the combination of features across multiple representations—when systematically integrated and optimized—can provide a practical, scalable, and interpretable alternative to more complex TSC approaches. The full implementation is publicly available at: https://doi.org/10.5281/zenodo.15695653 .