arXiv:2607. 04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost.
By Nicholas Tan Jerome, Frank Simon
arXiv:2607. 00958v1 Announce Type: new Abstract: Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often depends on view and augmentation choices that encode domain-specific invariances.
By Alexander Chemeris, Ming Jin, Randall Balestriero
arXiv:2608. 01586v1 Announce Type: cross Abstract: In recent years, numerous open-source software libraries have been developed for computing sets of features from univariate time series.
By Trent Henderson, Ben D. Fulcher
arXiv:2606. 27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.
By Augustinas Ju\v{c}as, Yangchen Pan
arXiv:2601. 22631v2 Announce Type: replace-cross Abstract: The application of data-driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data.
By En Fu, Yanyan Hu, Zengwang Jin, Kaixiang Peng
arXiv:2604. 02765v2 Announce Type: replace Abstract: Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplored.
By Zhiming Xu, Baile Xu, Jian Zhao, Furao Shen, Suorong Yang