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
The paper introduces the Clustering Occupation Kernel (CLOCK) method, a representation-based framework that embeds time series from dynamical systems into a reproducing kernel Hilbert space using a time‑averaged occupation kernel. This embedding allows trajectory comparison via the RKHS norm and, for differential equation systems, approximates the kernel mean embedding of the invariant Sinai‑Ruelle‑Bowen measure, effectively measuring the maximum mean discrepancy between attractors. CLOCK handles unequal‑length and irregularly‑spaced data, incorporates Takens’ embedding for general time series, and demonstrates superior performance over benchmark methods on synthetic data and real intracranial EEG recordings, with ablation studies confirming theoretical convergence and noise robustness.
By Michael L. Wells, Kamel Lahouel, Victor Rielly, Bruno Jedynak
MedFlow is a class‑aware multi‑scale flow matching framework designed to synthesize medical time‑series data. It uses a vector‑quantized multi‑scale tokenizer to capture both coarse and fine temporal patterns, and introduces Token Marginal Guidance to steer generation toward minority‑class characteristics. Experiments on four public datasets show MedFlow outperforms diffusion baselines, improving AUPRC by 5.8%, reducing Context‑FID by 88.6%, and achieving 3.8× higher sampling throughput.
By Yanhao Huang, Shibo Feng, Wanjin Feng, Peilin Zhao, Chunyan Miao
The Time-partitioning Gaussian Process Mixture Model (TPGPMM) is a Bayesian nonparametric framework that identifies persistent regimes and models nonstationary dependence in time series. It uses an adaptive Pitman–Yor prior on contiguous segmentations combined with regime-specific Gaussian processes, and learns change points and hyperparameters via a split–merge Metropolis-within-Gibbs sampler. Experiments on the CBOE Volatility Index and the 10‑year U.S. Treasury yield recover regime structures aligned with major market and monetary‑policy events, while variants that estimate observation noise improve predictive accuracy but may reduce sparsity or event alignment.
By Guilherme Piantino, Hedibert F. Lopes
arXiv:2609.37715v1 Announce Type: new
Abstract: Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more targe...
By Manh Nguyen, Minh Hoang Nguyen, Huu Hiep Nguyen, Van Dai Do, Hung Le
arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.
By Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk