arXiv Machine Learning
Aug 18

QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile

arXiv:2608. 15492v1 Announce Type: new Abstract: Finding representative waveforms in long time series has scientific and practical value in many domains, as it enables summarization and visualization of large time series datasets, and downstream tasks like classification and forecasting.

By Carlos H. Mendoza-Cardenas, Rogers F. Silva, Austin J. Brockmeier
arXiv Machine Learning
Sep 21

Time series generation with spectrally aligned latent flow matching

The paper introduces a spectrally-aligned latent-flow model for time‑series generation that trains the latent space to preserve dynamical properties relevant to synthetic data quality. By incorporating fine‑tuning losses based on Fourier, wavelet, and signature transforms, the method mitigates spectral mismatches caused by latent compression and ensures alignment with true signals in terms of smoothness and targeted spectral content. Experiments on real‑world long‑range univariate and multivariate benchmarks show that the aligned model outperforms a base latent‑flow model and state‑of‑the‑art approaches in signal realism, computational efficiency, and local structure alignment.

By Camilo Carvajal Reyes, Felipe Tobar