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arXiv:2502. 15637v2 Announce Type: replace-cross Abstract: While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly focused on forecasting.
arXiv:2601.20845v2 Announce Type: replace Abstract: Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require do...
arXiv:2506.08641v3 Announce Type: replace Abstract: Adapting vision models for time series analysis is compelling, yet all existing approaches are falling short of dedicated time series foundation mo...
SMart is a new time series representation learning framework that combines a multi-phase recurrence plot recovery task with a source dataset selector. The recovery task uses three alternative modes to guide the encoder in capturing time series dynamics, while the selector chooses multiple suitable source datasets to augment the target dataset during pre‑training. Experiments demonstrate that SMart surpasses state‑of‑the‑art models, reducing mean absolute error by up to 19.5% in regression and increasing classification accuracy by up to 1.34%.
arXiv:2607. 17653v1 Announce Type: cross Abstract: Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source data.
arXiv:2608. 13262v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored.