The paper introduces Chameleon, a channel‑dependent state space model for multivariate time series forecasting that allows data‑dependent, fine‑grained interactions across variables while maintaining linear scaling with the number of variables. By integrating selective state space models with a Kalman filter and adapting GatedDeltaNet as the backbone, Chameleon improves generalization and achieves lower MSE and MAE on strongly dependent ODE and PEMS datasets compared to both channel‑independent and prior channel‑dependent methods. Across 28 benchmark settings, it outperforms baselines in the majority of cases and demonstrates competitive training‑time and memory efficiency on Traffic and ETT datasets.
By Yu-Cheng Wu, Fan-Keng Sun, Li-Chun Lu, Duane S. Boning
arXiv:2606. 04752v1 Announce Type: cross Abstract: Transformers consuming multi-channel scalar signals must embed $C$ simultaneous values into one $d_{\text{model}}$-dimensional vector per time step.
By Ossi Lehtinen
arXiv:2606. 08935v1 Announce Type: cross Abstract: Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks.
By Kang Zhang, Wei Jian Lau, Shoushou Ren, Dong Lin, Joon Son Chung, Chuanhao Sun
arXiv:2609.14595v1 Announce Type: cross
Abstract: Multichannel time-series classification commonly assumes synchronized sensor streams, although latency, clock drift, and preprocessing can introduce...
By Sebastian Buschj\"ager, Michael Frichert, Daniel Kuhe, Jian-Jia Chen
arXiv:2604. 17388v3 Announce Type: replace-cross Abstract: Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables.
By Kadir-Kaan \"Ozer, Ren\'e Ebeling, Markus Enzweiler
arXiv:2605. 31259v2 Announce Type: replace Abstract: Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities.
By Alberto D. Cencillo, Leonardo Concepci\'on, Juli\'an Luengo, Isaac Triguero